A Little Birdie Told Me

Twitter and H1N1

Running head: TWITTER AND H1N1

A Little Birdie Told Me: H1N1 Information and Misinformation Exchange on Twitter Tonya Oaks Smith University of Arkansas at Little Rock

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Twitter and H1N1

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Twitter and H1N1 Acknowledgements American writer Cynthia Ozick said “We often take for granted the very things that most

deserve our gratitude.” I would not have truly engaged in the process that is the Applied Communication Studies Program in UALR’s Department of Speech Communication if I did not take the time to thank the myriad individuals who helped me along the way. Here, I would like to thank many people who were dedicated in their own ways to my success in graduate school.

My parents have always been the most supportive imaginable. I especially appreciate my mother’s strong example in earning her master’s degree when she had a family to take care of; she proved anything can be done with hard work and perseverance. Thank you both for believing I could complete this step in my educational journey. My husband and daughter deserve to have their names included as authors of this research paper as much as I do. They helped me check formatting and sources and dealt with my anxiety and sleep deprivation. I am thankful for their joining the collective and their sacrifices to get us where we are today.

Dr. Avinash Thombre has been a true inspiration in this process. His background is similar to mine, and we were able to make a great team when it came to interpreting Ev Rogers’ Diffusion of Innovation and applying that work to my research. The other professors on my committee – Dr. Rob Ulmer and Dr. Julien Mirivel – gave excellent advice to strengthen my arguments. I am indebted to each of these men – as well as the other professors in the Speech Communication Department – for their contributions to my education. Through you all, I have learned what Krishnamurti meant when he said, “There is no end to education. It is not that you read a book, pass an examination, and finish with education. The whole of life, from the moment you are born to the moment you die, is a process of learning.” I hope to learn from each of these professors’ examples and be a leader in the work for excellence in communication.

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Twitter and H1N1 In the completion of this project, I depended on a number of friends to read sections,

respond to questions, and tell me if my reasoning made sense to someone who was not a communication scholar. I so appreciate your indulgence of my work, and I commit here to returning the favor when needed.

Finally, I’d be remiss if I did not thank the multitude of people who inspire me each day with their work in the field of computer-mediated communication. With all of us working together, we will establish a generation of ethical communicators who just happen to practice their craft on their Internet. I’m incredibly proud to be a part of that group of individuals striving for excellence in communication every day.

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Twitter and H1N1 A Little Birdie Told Me: H1N1 Information and Misinformation Exchange on Twitter Today, people expect to share information, not be fed it. They expect to be listened to when they have knowledge and raise questions. They want news that connects with their lives and interests. They want control over their information.

And they want connection – they give their trust to those they engage with – people who talk with them, listen and maintain a relationship.

– Michael Skoler, 2009, p. 39 Since 1997, computer programmers have worked to develop social networks that make it

possible for users to connect with others who share common interests. The first of those networks, SixDegrees.com, allowed individuals to establish profiles, list friends, and connect with others who have similar interests and contacts (boyd & Ellison, 2007). In effect, the advent of social media allowed users to share information, just as Skoler noted. Individuals began to control the data they received (Skoler, 2009). The phenomenon has grown since then to include sites focused on music, job hunting, buying and selling used items, and blogging (boyd & Ellison, 2007). One of the newest introductions into the world of social networks is Twitter, a microblogging site that restricts user posts to 140 characters or less. According to Twitter’s chief operations officer, in June, the service boasted 190 million users who post about 65 million messages per day (Schonfeld, 2010). This number is growing almost exponentially each month; in April, 180 million users were recorded (Schonfeld, 2010).

Developing connections among individuals with common interests is only one way that social networks are used. The worth of the Internet has increased as individual users have realized the value of connecting with other people, changing the perception of the Web from “a one-way broadcasting or publishing medium” to a one that allows individuals to create valued interpersonal networks (Gordon, 2009, p. 7). Internet-based communication channels can also

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pass along information to consumers and diffuse data to a group of individuals who are in one’s inner circle. Twitter helps users “make better choices and decisions and, … creates a platform for [users] to influence what’s being talked about around the world” (Twitter, 2010). Individuals around the world use Twitter to learn and then share their knowledge with other users. Therefore, the medium stands as a one of the powerful new ways we use the Internet to diffuse information within networks of individuals who are alike in their beliefs (Rogers, 2003).

Ev Rogers’ theory of Diffusion of Innovation focuses on diffusion as a “process by which an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 2003, p. 11). Twitter is not only a new innovation itself, it is a prime communication channel many individuals use to share information about their lives and interests.

In particular, users employ Twitter to share news events – either with a small impact like the birth of a child or large impact like the spread of the H1N1 virus. Social media, including Twitter, have dramatically changed how individuals share and receive information and news (Ludtke, 2009).

With this research project, I analyzed the diffusion of information about H1N1 flu on Twitter. I used the theoretical lens of diffusion of innovation to examine the information-sharing behaviors of individuals on Twitter. To begin, I briefly explain the new communication phenomenon known as Twitter and the H1N1 virus as well as its progression throughout the world. Then, I examine the research that other scholars have completed on computer-mediated communication, diffusion of innovation, and health communication. Next, I outline the research methodology followed in order to examine how individuals used Twitter in the midst of a worldwide health crisis. The paper details content themes within the online discussion of H1N1 and then I draw parallels between individuals’ use of Twitter and how this use of the medium

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helped these people make a decision on whether or not to vaccinate themselves and their families. Finally, I offer analysis of what the content analysis and survey data mean as well as suggestions for the future use of Twitter to communicate pertinent information – particularly health information – more effectively as a part of a well-rounded communication plan designed to diffuse innovations and change behaviors.

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Twitter and H1N1 A Twitter Primer Twitter was developed in 2006 after years of work by co-founders Biz Stone and Evan

Williams (Malone, 2009). Twitter allows users “to post short text messages – called ‘tweets’ – of no more than 140 characters on their personal feed” (Malone, 2009). The innovation is commonly called microblogging, and an individual’s followers can read posts. Since its inception, the tool has been used to communicate the mundane – information about what an individual has eaten for dinner – to the incredibly important – information on the forced landing of American Airlines flight 1549 in the Hudson River (Malone, 2009). Indeed, during the 2009 presidential elections in Iran and subsequent citizen revolt, Twitter was the only way residents could get information to the outside world about the government’s actions (“Twitter links Iran,” 2009). Apparently, Iranian officials did not assign much importance to this new innovation, but its power is growing exponentially as the number of adopters grows.

Twitter can be used to talk to one individual or a small group, in the same fashion as interpersonal communication, or to millions, in the same way as mass media are used. In fact, the medium allows individuals to embrace the old-style idea of journalism and interpersonal communication, one that delivers news to help readers “connect with neighbors, be active citizens, and lead richer lives” (Skoler, 2009, p. 38). The innovation has been wholeheartedly embraced as a result of this change in thinking about communication – the movement from oneway to reflexive (Gordon, 2009). In April 2009, over 7 million unique visitors used the site, proving the application’s reach and influence in the social networking community (McGiboney, 2009). Currently, Twitter users share 65 million messages per day (Schonfeld, 2010). Thus, Twitter shows promise for communicating useful information to multitudes of people in real time, as shown in Figure 1 below, completed with information from Compete.com, a web

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analytics company that monitors the use of websites with surveys of over 2 million Internet users in the United States. These users gave the company their permission to analyze their web usage as well as conduct surveys into their habits on the Internet (Compete.com, 2010). This communication tool roughly follows the traditional S-shaped curve that Rogers (2003) states innovations will adhere to during the innovation-adoption process.

Despite the fact that Twitter can – and is – used to share positive information, the tool can also be used to spread misinformation, as can be seen in the beginning of the H1N1 outbreak in spring 2009. Journalists commented in April 2009 on the microblogging site’s becoming “a hotbed of unnecessary hype and misinformation about the outbreak” (Sutter, 2009). Tweeps, as those who post on the site have become known, spread information about the false connection between consuming pork and the flu, the possibility of germ warfare, and other assertions about the disease and its spread (Day, 2009; Morozov, 2009). The ease with which misinformation can be spread, as well as the possibility of information overload, or “the state of an individual or a system in which excessive communication inputs cannot be processed and utilized, leading to breakdowns,” are two of the prime problems seen with using Twitter as a communication vehicle for important information (Rogers, 2003, p. 368-369).

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Users in Millions

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Figure 1. Graphical representation of the Twitter adoption curve – an S-shaped curve.

Data course is Compete.com.

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Twitter and H1N1 H1N1: The Health Crisis When a crisis occurs, individuals instinctively seek information that will help them

alleviate their uncertainty. They want to acquire data that will help them process their situation and respond effectively to the danger it presents (Ulmer, Sellnow & Seeger, 2007). The H1N1 outbreak, which began in April 2009, is no different than other health crises such as the Severe Acute Respiratory Syndrome (SARS) outbreak of the early 1990s or the Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) outbreak in the 1980s. Many individuals sought to create self-efficacy, the perception of an individual’s “capacity to organize and execute the actions required to manage prospective situations” (Singhal & Rogers, 2003, p. 313-314). However, with the latest health crisis that the H1N1 virus outbreak presented, a number of new communication media were available that simply did not exist in the late 20th century. Twitter was one of the many ways that individuals employed to collect information on the virus and how to avoid contracting it. Both the World Health Organization and the Centers for Disease Control and Prevention utilized the new medium to communicate timely information on the virus to their followers.

When the H1N1 virus was first diagnosed as a unique illness in April 2009, the Internet came alive with stories from around the world of those who were sick with the illness. The first documented death occurred in Oaxaca, Mexico, and health officials there declared the death as an isolated incident, even though individuals who had been in contact with the deceased woman were suffering mild symptoms of pneumonia (World Health Organization, 2009a). Soon after, there were other deaths from the same illness, which was then feared to be avian flu, and the Mexican government reported the illnesses to the World Health Organization (WHO). Some American travelers returned home from Mexico with symptoms of the mystery illness, and they

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were advised to stay home until a cause could be found (Chen, 2009). Canadian health officials determined – after studying samples sent from Mexico – that the virus was not avian. Instead, it was found to be the H1N1 “swine flu” virus (WHO, 2009a). Later in April, the first H1N1 cases were reported in the United States, and the WHO declared a health emergency on April 26. At that time, there were a total of 40 cases of the H1N1 virus in the United States (Cable News Network, 2009).

The number of H1N1 cases rose almost exponentially in the following months. In June 2009, 74 countries had verified H1N1 infections, and WHO director general Dr. Margaret Cho declared the virus outbreak a pandemic (WHO, 2010). The virus was different in that it caused high rates of infection in the summer, when most viruses are largely dormant. The virus was also unique from other seasonal flu outbreaks, according to the WHO, because pandemic H1N1 was a new virus when it emerged and most people had no or little immunity to it. In addition, one of the lessons from history is that influenza pandemics can kill millions. Finally, there was no pandemic influenza vaccine at the outset (WHO, 2010).

In addition, individuals who were not normally susceptible to flu hospitalization and death – namely young adults – experienced the highest percentage of deaths by age group (See Figure 2).

After the initial surprise of discovering that a new H1N1 virus was running rampant in many countries, both the CDC and the WHO worked to respond to the crisis by sharing information with those who might be negatively impacted. They utilized unique approaches to communicate information, and through their work, uncertainty was alleviated (Ulmer et al., 2007). Both organizations posted information regularly to their Twitter feeds – twitter.com/whonews and twitter.com/CDCemergency – and held timely press briefings. Their

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work to ease uncertainty positively impacted how individuals dealt with the crisis (Littlejohn & Foss, 2008).

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120 100 80 60 40 20 0 0-4 years 5-24 years 25-49 50-64 >65 years Unknown years years Age of Deceased

Figure 2. Bar chart of H1N1 deaths in the United States by age group. Source is the Centers for Disease Control and Prevention.

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Twitter and H1N1 Reviewing the Literature In recent years, much research has been conducted on the formation of opinions through

the use of computer-mediated communication (CMC) and social networks (Black, 2007; boyd & Ellison, 2007; Lyons & Henderson, 2005). Twitter, however, is such a new phenomenon that scholarship has not caught up with the technology. The body of work instead focuses on the construction of online personas and the use of social media to spread marketing information (Neff, 2009). Since Twitter and other social networks are computer-mediated communication, we will apply the same theories and techniques that other researchers have used in its analysis.

In addition to the formation of networks with CMC, communication research touches on computer-mediated spread of misinformation, without connecting the construction of collective truth and conversion to action through false information (Black, 2007; Eastin, 2001). Health communication literature also focuses on the use of mass media to diffuse innovations and encourage healthy behaviors. However, this section of the literature does not include communication mediated by computers. In short, the literature fails to connect the dots between traditional mass media and new media and their shared use as cooperative channels in the spread of health information.

Sense Making Authors have examined sense making with computer-mediated communication, specifically Twitter, as well as the overload of data available in this channel (Farhi, 2009). In addition, researchers have examined the information overload in the light of news consumers’ attempts to become active participants in the process of constructing news (King, 2008). The process of gathering and productively using information in the digital age has changed dramatically, and users must work to determine which information is true and which is false

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(King, 2008). Researchers, however, offer no way to determine the veracity of data passed along through digital channels or discuss the process by which users determine which information allows them to develop self-efficacy (Botta, 2006). There is, however, a focus on reader acceptance and contribution of information and misinformation to the new-media stream (King, 2008).

Researchers also attempt to provide a basis for how journalists and other news-gatherers – including other Twitter users – regard the new medium. Companies respond daily to misinformation spread through Twitter – either intentionally or unintentionally (Neff, 2009).

Their responses – and the analysis of these defensive communication acts – helps provide best practices for response to misinformation could be used by for-profit organizations or government agencies who are responsible for disseminating information about health crises in the digital age (Eastin, 2001; Neff, 2009). Research has, however, contributed to the understanding of how truth is constructed through public discourse, and this theory can be applied to analysis of new media such as Twitter (Black, 2007).

Black, specifically, focuses on the formation of public knowledge and truth. His argument centers on “how information, both factual and nonfactual, can evolve into truths within the realm of public knowledge” (Black, 2007, p. 2). The researcher notes several instances of how misinformation has been spread through word of mouth and mass media, entered into the public consciousness as truth, and been acted upon. Black (2007) makes use of diffusion of innovation theory in his argument, specifically the use of interpersonal communication to spread ideas that then make their way into the public consciousness (Rogers, 2003).

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Computer-mediated Communication The literature also explores the differences between computer-based opinion leaders and opinion leaders from more traditional environments (Lyons & Henderson, 2005). While the characteristics for both groups are similar, computer-mediated opinion leaders are more exploratory in their behaviors (Lyons & Henderson, 2005). Though the research provides basic information on opinion leadership in a computer-mediated world, it does not examine the blurring between interpersonal communication and mass media that computers allow those who choose to use them to communicate. In addition, we have seen a contrast between the negative and positive aspects of information sharing via CMC (boyd & Ellison, 2007; Eastin, 2001).

Media Intervention in Health Behaviors Finally, much research has focused on media intervention in health behaviors, specifically when individuals do not have enough interpersonal communication support to form opinions (Singhal & Rogers, 2003; Botta, 2006). By applying the media dependence theory in conjunction with research on the diffusion of innovation, Botta (2006) discusses the importance of mass media when individuals have unmet information needs. The added information – delivered by mass media – provides these individuals with messages of self-efficacy (Botta, 2006). In this article, the author focuses on behaviors associated with HIV and AIDS prevention, but the theory could be applied to any health crisis. Information’s empowering force – especially when delivered in a trusted forum – allows individuals to make intelligent decisions regarding their health during crisis.

Each of these studies shows that information and misinformation are easily spread through both mass media and interpersonal communication channels, including new media channels. However, Twitter is such a new innovation – it has not yet become completely diffused

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itself – that scholarship has not focused on it in a concrete way. Future researchers need to extend focus beyond traditional mass media and examine new media in the same fashion. This research project in part attempts to examine Twitter in the same light as researchers have studied other communication channels.

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Twitter and H1N1 Theoretical Framework As an overarching theoretical tool, this research project uses Everett Rogers’ extensive

conceptualization of how an innovation spreads in a social system among its members. As defined earlier, diffusion is the way that a new idea is shared through communication channels.

This spread takes place over time and throughout a social system (Rogers, 2003). However, diffusion is more than simply wanting to ensure that a new idea will be shared. Instead, the best examples of diffusion of innovation show that an idea has been adopted (Dearing & Meyer, 2006). Future scholars noted that education and mass media directly contributed to this idea of successful innovation through adoption (Melkote, 2006). Whether that idea is planting a newly developed kind of seed corn or using a condom to prevent the spread of AIDS, those who diffuse the innovations want to ensure that the idea is accepted and adopted quickly (Singhal & Rogers, 2003).

There are several concepts within the theoretical framework that help us understand the use of Twitter to diffuse information and how its use can help influence future health behaviors.

In this section of the research, I will focus first on the innovation-decision process and how individuals follow this path to adopt or reject innovations. Then, I discuss communication networks and why they are formed and used to diffuse information about innovations. I will also utilize the concept of opinion leaders, showing how they are differentiated from change agencies and agents. Next, I examine the differences between mass media and interpersonal channels, discussing how each of these communication tools is used in different ways to diffuse innovations. Finally, I will focus on disinformation and misinformation and how their use can cloud the perceptions of message receivers, changing their minds on adopting innovations.

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Innovation-decision Process The process of determining whether or not an innovation should be accepted is known throughout Rogers’ work as the innovation-decision process (2003). This five-step program (See Figure 3) allows individuals to first work through a knowledge phase, where he or she learns of an idea or innovation and how it works (Baumann, 2008). During this phase, “potential adopters develop perceptions of the innovation characteristics, which are influenced by peers, change agents, mass media portrayals, social norms, the kinds of innovation information needed, initial experiences, and, in some cases, the adoption by others” (Rice, 2009). The individual then moves through the persuasion phase, where he or she forms an attitude toward the innovation (Rogers, 2003). Then, the prospective adopter actually makes a decision and implements it in the third and fourth stages of the process (Baumann, 2008). Finally, the decision and implementation must be reinforced in the confirmation stage (Rogers, 2003).

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Figure 4. Representation of the five stages in the innovation-decision process. Adapted from Ev Rogersʼ Diffusion of Innovations.

Communication Networks According to Rogers, communication is the “process in which participants create and share information with one another in order to reach a mutual understanding” (Rogers, 2003, p.

5). The mutual understanding happens as communication brings together those who are in similar circles – because of socioeconomic status, learning, or other factors. These communication circles allow individuals to come to a conclusion about an innovation – namely whether to adopt and use it or not. Information by itself cannot help an individual come to the conclusion to adopt an innovation. Instead, personality – the very charisma that helps construct an individual’s position as an opinion leader – is also necessary (Dearing & Meyer, 2006).

Integral to the idea of effective diffusion of innovation through communication and shared

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agreements is the concept of a communication network, or a group of individuals who are connected by sharing information on topics in their common interest (Rogers, 2003).

The effectiveness of communication networks can frequently be determined by the level of homophily, or the “degree to which a pair of individuals who communicate are similar,” of the individuals within those networks (Rogers, 2003, p. 305). While it usually adds to the level of diffusion, homophily can sometimes serve as a barrier to innovation because individuals who are similar in beliefs and behaviors do not interact with those who would most benefit from the introduction of innovation (Leonard, 2006). For instance, those of higher status – the ones most likely to encounter new innovations – rarely interact with those of lower status. Groups of individuals who are too similar are also not as creative as groups of people who are slightly different. These individuals simply don’t have to be creative. Their ideas are the same as everyone else’s, and there is no reason to venture beyond their comfort zone to find remedies for problems. Therefore, individuals who are too similar to their peers will lose the ability to serve as opinion leaders and persuaders (Leonard, 2006). Parallel thinking dilutes potential opinion leaders’ power and can bring the diffusion process to a standstill.

Contrary to homophily, heterophily is the dissimilarity between communication partners (Rogers, 2003). Differences in opinions can cause cognitive dissonance, but these differences can inadvertently work to strengthen communication between diverse cliques (Rogers, 2003). In fact, in today’s rapidly changing world, creative friction is often necessary to inspire inventors to create new products and processes (Leonard, 2006). However, despite the advantages that infrequent communication and differences across social and economic boundaries can have to help diffuse innovation, Rogers believed that homophily among interpersonal communication networks is one of the greatest engines for change. Opinion leaders pilot these networks, and

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their effectiveness is gauged on the “degree to which [they] are able to influence other individuals’ attitudes or overt behavior informally in a desired way with relative frequency” (Rogers, 2003, p. 27). Because these influential individuals are the ones who largely drive the diffusion process, in the next section, I elaborate on the definition of opinion leaders.

Opinion Leaders Change agents are the first individuals charged by a change agency – or an organization working toward the adoption of an innovation – but they are frequently unable to directly impact individual behaviors. Opinion leaders serve as the sergeants in the fight to diffuse innovations in a system. They are closer to the average foot soldiers – and command these individuals’ respect – than those who are at the top of the chain of command, or the change agents and agencies. In much the same way a general’s ability to win a war is dependent on his sergeant’s ability to carry out orders and influence others to carry them out, the success of a change agent is directly and “positively related to the extent that he or she works through opinion leaders” to achieve the agency’s goals (Rogers, 2003, p. 388). Because opinion leaders are closer to the actual prospective adopters, they are able to impact behaviors more quickly and directly than the actual agents of change. As stated above, Rogers believes that opinion leaders can share the innovation through homophilious or heterophilious communication through these channels. Homophily, however, is the strongest method of sharing information and converting behaviors. A successful opinion leader must walk a fine line between being similar to those he works for and those he is working to persuade. If diffusion is to be successful, opinion leaders must bridge the gap between those who have diverse bodies of knowledge (Leonard, 2006).

Contrary to many change agents’ beliefs, those who adopt new innovations the quickest do not often serve as true opinion leaders in a community. Opinion leaders are not innovators,

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nor are they the first individuals to adopt an innovation or make a change (Rogers, 2003). These people – early adopters – are frequently seen as deviants within a social system and do not garner the respect that true opinion leadership commands (Rogers, 2003). Instead, opinion leaders are individuals who have followers. They are respected in their community, and they are “sought by others for their opinions and advice” (Lyons & Henderson, 2005; Singhal & Rogers, 2003).

When change agents are able to train influential individuals and send them out to spread the message, then more persuasion is accomplished and innovations are adopted successfully.

Opinion leaders can share information in many ways, among them mass media and interpersonal channels, which are discussed below. But one of the most effective ways for these persuaders to share their knowledge is through a demonstration (Rogers, 2003). In fact, when AIDS ran rampant through the gay community in the 1980s, the most effective ways of spreading information about the effectiveness of condom use was through demonstrations and interventions held in gay bars (Singhal & Rogers, 2003). Demonstrations help to increase the observability of advantages involved in an innovation – one of the requirements for adoption according to Rogers’ theory. Demonstrations are often effective, Rogers said, because they add the “perceived competence credibility of the change agent with the perceived safety credibility of the demonstrator” (Rogers, 2003, p. 390). Demonstrations are even more effective in creating behavioral change or achieving adoption if the demonstrator is an opinion leader, or one who is trusted to share authentic information about the innovation and the results of the demonstration with those who fall later in the adoption cycle (Rogers, 2003). When the opinion leader is outfitted with pertinent information and supplies from the change agent, then he or she is also better able to share ideas and persuade individuals to adopt innovations (Adhikarya, 2006).

Demonstrations and pertinent information can be counted as two of the ways opinion leaders

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engage in interpersonal communication with their target audiences. In the next section, we discuss mass media and interpersonal channels, as well as the differences between the two and how new methods of communication are serving to blur the lines between mass and interpersonal communication channels.

Mass Media and Interpersonal Channels In addition to utilizing demonstrations, opinion leaders can spread information through mass media or interpersonal communication. Mass media channels, usually the most efficient way to talk about innovations, are perceived to be the “magic multipliers of development benefits, and as harbingers of modernizing influences” (Melkote, 2006, p.151). Interpersonal channels, however, “involve a face-to-face exchange between two or more individuals” (Rogers, 2003, p. 18). The informal influence that opinion leaders exert often results from “productrelated conversation, referred to as ‘word-of-mouth’ communication” (Lyons & Henderson, 2005). In his theory, Rogers emphasizes the importance of both mass media and interpersonal communication channels in sharing information during the course of a diffusion project. Opinion leaders also have the opportunity to share pertinent information through interactive communication via the Internet, and this method of communication has become “more important for the diffusion of certain innovations in recent decades” (Rogers, 2003, p. 18). Scholars often refer to this method of communication as “word of mouse,” and much study remains to be conducted if researchers and change agents are to understand the power of computer-mediated communication as either a mass medium or interpersonal channel (Lyons & Henderson, 2005).

Each channel has its own strengths and weaknesses, and change agents must determine which one will be more effective in ensuring that an innovation will diffuse. In discussing the innovation-decision process, Rogers asserts that “mass media channels are relatively more

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important at the knowledge stage, and interpersonal channels are relatively more important at the persuasion stage” (2003, p. 205). Researchers have found recently, however, that mass media can substitute for interpersonal channels in certain circumstances, such as when an individual does not have access to expert interpersonal communication (Botta, 2006). These two channels, while very different, are connected by their focus on sharing message content that is “concerned with a new idea” (Rogers, 2003, p. 18). Differences, especially in the age of more computer-mediated communication and shrinking boundaries between communities, may be disappearing. The Internet provides opinion leaders with both “an unprecedented repository of information” on a number of subjects and the ability to share that information with an untold number of individuals (Lyons & Henderson, 2005, p. 321). The innovation that we know as the World Wide Web has opened up a new idea of opinion leadership and blurred the lines between social classes and familiar groups.

How the spread of information is accomplished and how that information diffusion leads to adoption of innovation figures heavily in this research. The discussion of opinion leaders and the examination of new communication technology bring us to the following research questions: RQ1: How is Twitter used as a communication channel for H1N1 information diffusion?

RQ1a: How are opinion leaders determined on Twitter? What constitutes an interpersonal network on Twitter?

Interpersonal communication and mass media channels are focused on sharing information, or data that can change the level of uncertainty in a given diffusion situation (Rogers, 2003). However, in order to effectively share an innovation, the information that is shared must be accurate. The Internet has no “government or ethical regulations controlling the

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majority of its available content” (Eastin, 2001). Because information is not verified, audience members are forced to distinguish for themselves between accurate information and misinformation (Eastin, 2001).

Misinformation and Disinformation Governments have used misinformation and disinformation for centuries to control the hearts and minds of their citizens and others commonly thought outside the normal sphere of influence (Hachten & Scotton, 2007). Most often used during war efforts, misinformation is employed to help control messaging, and the media are not immune to the ready flow of incorrect information. While the aim of spreading incorrect information may be seen to be negative, it has been used to positive ends. Of course, the perception of those ends depends on which side of the conflict the audience supports. During Operation Iraqi Freedom, for example, information has been tightly controlled, and some official news “was actually disinformation intended to mislead the enemy, not to inform the public” (Hatchen & Scotton, 2007).

Broadcasters, however, acknowledge the need to give accurate information to viewers and listeners (Clark & Christie, 2005). In fact, later in the Iraqi War, reporters and broadcasters whose information was passed along in the Middle East recognized the importance of sharing truthful information and established outlets to do just that (Clark & Christie, 2005).

The use of computers to share stories has muddied the water for those who seek information from opinion leaders and other authoritative sources. While misinformation and disinformation have always been available through both mass media and interpersonal networks, the quickness with which individuals can communicate via computers has increased the amount of incorrect information that can be shared with anyone and everyone all over the world (Seidel & Rogers, 2002). Information innovations “have revolutionized the speed of information and

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provided global reach coupled with easy affordability and accessibility for large portions of the world population” (Mohammed & Thombre, 2003). There are several reasons that misinformation and disinformation are so easy to share online. First, the Internet is a costeffective way to share data, and individuals who would serve as self-appointed opinion leaders are no longer required to be members of news-gathering organizations or to show credentials for their presumed expertise (Carmichael, 2003). Second, individuals and organizations working as change agents or opinion leaders are able to publish “Websites with apparently greater authority and with a potentially far larger audience than would otherwise be the case” (Carmichael, 2003).

While many embrace the effortlessness with which individuals can share information via the Internet, this ease of exchange can negatively impact diffusion of innovation as well if misinformation is shared.

Social media networks are prized for their focus on authenticity. But just as interpersonal exchanges can share fallacies, the Internet can be a tinderbox for misinformation that causes a wildfire in today’s rapid communication environment. If individuals have the capacity to determine which individuals they will follow through Twitter, then researchers should determine the answer to another research question: RQ2: How do people on Twitter distinguish its credibility or lack thereof, and how does this credibility influence their behavior?

Researchers have long engaged in studying news events for their salience (Seidel & Rogers, 2002). The H1N1 outbreak surprised scientists and health officials with its speed and ferocity, and its propensity to attack individuals who were not normally at risk for death from a flu virus (WHO, 2010). In the beginning of the outbreak, the virus was a totally new phenomenon, and there was a great deal of uncertainty for both officials and regular citizens

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about how the virus was spread and how best to prevent that spread. Therefore, this news event was highly salient for most individuals around the world.

In the same way, Twitter – and research on its adoption and use – has its own salience.

New media have recently become the topic of much more research (Tomasello, Youngwon, & Baer, 2009; Kim & Weaver, 2002). Millions of individuals use the microblogging service each day to send short messages to millions of other individuals. It is one of the newest methods of communication, and people are still learning about its use in everyday life. In addition, Twitter and other social media applications offer the opportunity for interactivity, which allows for stronger relationship development and is considered a “central influence upon the outcomes communicators take away from the interactions” (Ramirez, 2009, p. 301). The first stages of Twitter adoption and the H1N1 virus outbreak share roughly the same timeframe. Each of these innovations is worthy of study on its own. However, when two such important advances collide in the way that Twitter and H1N1 did in mid-2009, researchers should take notice. In the following section, I explain the methodology for exploring the significance of the diffusion of information on H1N1 via Twitter.

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Twitter and H1N1 Methodology Both prior research and the theoretical framework of diffusion of innovation led to the

aforementioned research questions. In order to answer these questions, I defined a research methodology that examines both the content that Twitter users choose to share and their selfreported habits in communicating that information. To conduct research on the prevalence of correct information and misinformation on the H1N1 outbreak that was shared via the web-based communication tool known as Twitter, I employed a three-step data collection process. First, I gathered data from Twitter and performed a content analysis. Next, I conducted a survey of random Twitter users on their habits while utilizing the microblogging platform. Finally, I performed interviews with a sub-sample of those who answered the initial survey in order to further discern their Twitter use and behaviors. In the following section, I will describe each of these steps in more detail, explaining the reasoning behind each of the research segments.

Step 1 – Content Analysis of Tweets To begin, I collected individual posts from Twitter. This dataset – all Tweets ever posted to the site – was randomly searched for postings that mentioned at least one of three terms: Swine flu, swineflu, and H1N1. The resulting dataset numbered approximately 300,000. In order to further reduce the number of posts to be examined, I isolated the Tweets sent on three key dates – April 25, Sept. 4, and Oct. 24, 2009.

These dates were chosen for their relative importance in the progression of the virus throughout the world. On the first date, April 25, 2009, the World Health Organization met to discuss the epidemic and ways to deal with virus treatment and prevent the spread of the disease (MSNBC.com, 2009). On Sept. 4, 2009, the second date in question, the number of deaths around the globe ramped up, and the virus killed 625 people in the week prior (MSNBC.com,

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Twitter and H1N1

2009). On the third date, Oct. 24, 2009, President Obama declared a national emergency to deal with the flu outbreak, freeing up significant resources to deal with the issue (MSNBC.com, 2009). On each of these dates, Twitter traffic concerning the outbreak swelled in comparison to Tweets about other topics. This increase in traffic led me to believe that the events occurring on these three dates were important among Twitter users. By narrowing the dataset to these three dates, I reduced the number of Tweet posts to be examined to 46,000. I determined this number was representative of the original 300,000 as well as more manageable than the original dataset, so I began the process of content analysis.

To start the content analysis, or the “procedure that helps researchers identify themes and relevant issues often contained in media messages,” I read one-third of the dataset attempting to avoid bringing preconceived notions about the Twitter posts’ themes (Rubin, Rubin & Piele, 2005, p. 223). By viewing the tweets, or “data as representations not of physical events but of text, images, and expressions that are created to be seen, read, interpreted, and acted on for their meanings,” I drew parallels between pieces of information from disparate sources (Krippendorf, 2004). From this content emerged common communication themes relating to the reasons individuals share information via computer-mediated communication. After reading these 15,000 tweets, I was able to determine that Twitter users focused much of their online conversation on four major topics.

Interest in content analysis is tied not only to the topics inherent in the individual posts but also to the effect that the content has on those who send it and receive it (Rubin et al., 2005).

After determining the topics, therefore, I had to categorize those ideas into communicationseeking or -giving posts. This classification is akin to Burgoon’s principle of interactivity, which holds that “human communication processes and outcomes vary systematically with the degree

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Twitter and H1N1

of interactivity that is afforded or experienced” (Ramirez, 2009, p. 302). Posts that noted interactivity – or a desire for interactivity – were further categorized into one of three major communication themes – health information, uncertainty reduction, or misinformation and disinformation.

Step 2 - Survey Following this content analysis, an online survey was designed to ask Twitter users on how much H1N1 information they have obtained and the actions they have taken as a result of that information was conducted. The survey was self-administered, and no prompting for particular answers was given (Rubin et al., 2005). In order to encourage individual Twitter users to participate in the survey, which was based on the online survey tool Survey Monkey, I posted a Tweet requesting participation in a short survey about Twitter attitudes. I also asked for followers to retweet the original post – “Help with research on Twitter and communication.

Survey here:” – and several assisted me with communicating information about the survey to as many individuals as possible. After posting the request for participation once a week for a month, I received 59 responses to participate in the survey. Only 42 of these 59 completed the whole survey.

The survey form (Appendix A) was chosen to allow collection of individuals’ “attitudes, opinions, and reported behaviors or behavioral intentions” (Rubin et al., 2005) about how they use Twitter as a communication tool. Among the topics of interest in the survey were closedended questions on the number of followers an individual user has – or how potentially large his or her reach is – and open-ended and closed-ended questions on retweeting – passing along of pertinent information between related users. In addition, I asked open-ended questions to determine how long individuals had engaged in Twitter as a social medium, and how their – and

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Twitter and H1N1

responses to prior tweets. In addition, I asked if individuals had received a vaccination for H1N1 and whether or not information they received via Twitter helped them reach a concrete decision on vaccination for themselves and their families. This analysis helped determine how individuals who use Twitter are able to serve as opinion leaders, and whether Twitter is being used as a channel for interpersonal communication between related community members or if it is being used more as a mass media channel with information being pushed out by subject matter experts.

Lastly, the survey helped ascertain whether or not Twitter was being used effectively as a tool to create successful diffusion of information about H1N1 and the innovation of preventative vaccinations for the pandemic flu virus.

Step 3 - Interviews To complete the analysis of individuals’ personal and unique use of Twitter as a health communication and uncertainty-reduction medium, I lastly conducted 10 short telephone interviews comprised of two questions only. These interviews allowed qualitative answers to the “why and how come questions” that concerned me when I examined the survey results. In order to obtain a group of respondents for this section of the research, I again used Twitter to request participation. I sent out an appeal for those who already responded to my survey to contact me via direct message to further explain their involvement with Twitter. I contacted the first 10 individuals who responded and asked them two questions over the telephone: •

Did you or your family obtain the H1N1 flu vaccination?

•

How did reading information on Twitter contribute to your decision to get this vaccination?

In these interviews, I was able to obtain more qualitative data about where these respondents’ information about H1N1 and vaccinations to prevent the spread of the disease came

32

 

Twitter and H1N1

from. By asking if individuals had obtained the vaccinations for themselves and their families and where their information came from, I could further discern which information source served as the tipping point for vaccinations and whether that information was supplemented with faceto-face interactions (interpersonal communication) or news reports (mass media).

33 

Twitter and H1N1 Results – Twitter Themes Rogers’ innovation-decision process provides a framework for the analysis of Twitter

posts concerning H1N1 on the three key dates in the spread of the virus mentioned earlier. The process allows an individual to go from being aware or learning about something that is new to making a decision to use or not use it. Then, the individual must have his or her decision confirmed. Rogers’ definition of this frame of understanding consists of five stages – knowledge, persuasion, decision, implementation, and confirmation (Rogers, 2003). These five stages are outlined in Figure 5 below.

Figure 5. Representation of the five stages in the innovation-decision process. Adapted from Ev Rogersʼ Diffusion of Innovations.

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Twitter and H1N1 During the knowledge stage of the process, individuals must receive and comprehend

enough information to be able to begin to make a rational decision on the adoption of an innovation (Rogers, 2003). Mass media – like Twitter – work to create awareness-knowledge, while interpersonal communication can be used to tie individuals together and begin to help individuals enter the persuasion stage of the cycle. Though different, interpersonal communication also involves the transfer of knowledge. In fact, all innovation diffusion processes begin with the acquisition of knowledge from a vast array of sources (Leonard, 2006).

During the information stage, individuals are able to discuss the innovation within their social systems and start to gain support for their change in behavior from inside the system. Mass media can also share information about innovations. Twitter, however, can serve as both of these communication channels, guiding individuals through the information-gathering step in the process (Seidel & Rogers, 2002). After the information stage, individuals will enter the persuasion stage, where they form a good or bad attitude, or “organization of an individual’s beliefs about an object that predisposes his or her actions” (Rogers, 2003, p. 174). In his research, Rogers focused on the idea of a preventative innovation as one that would help an unwanted future event from happening, such as birth control to control unwanted pregnancies. In this same way, H1N1 vaccinations serve as preventative innovations to help stop the spread of the swine flu influenza. Mass media and interpersonal communication continue to influence an individual’s decision – they persuade a person to accept a preventative innovation like a flu shot.

Twitter also serves a purpose in this stage of the innovation-decision process by providing additional information to help persuade potential users.

Next, individuals enter the decision stage of the innovation-decision process. At this point, each person has to make a determination to try out the innovation – or not. Each individual

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Twitter and H1N1

has a different threshold of information that must be acquired in order to make a decision about an innovation (Dearing & Meyer, 2006). An individual will choose to adopt or reject an innovation, sometimes trying out that new idea or tool on a partial basis (Rogers, 2003). In this research project, the decision stage was represented by a determination whether or not to obtain an H1N1 influenza vaccination. Each step of the research for this project was designed to follow an individual Twitter user through the steps of the innovation-decision process and determine whether or not the new method of communication helped that person reach the decision to vaccinate or not.

A content analysis was undertaken to determine the information individuals and organizations were sharing on Twitter about the H1N1 influenza pandemic in late 2009.

Individuals certainly communicated about the virus in great detail during the outbreak, but what types of information were they sharing? A content analysis, where the researcher examines textual information for patterns, was the tool chosen to find out main topics and themes for H1N1 information on Twitter (Krippendorf, 2004). The analysis helped lead me to an answer for the first research question – How is Twitter used as a communication channel for H1N1 information diffusion?

To ascertain the pertinent topics and communication themes, I analyzed 46,000 Tweets that users posted about the H1N1 pandemic flu virus outbreak for prevalent themes. Through this content analysis, which is the process of drawing similarities between information from different sources (Krippendorf, 2004), I was able to identify three broad umbrella themes concerning the H1N1 virus on Twitter – health information, uncertainty reduction, and misinformation. Each of these three themes further divided into sub-themes – deaths, vaccinations, symptom identification, and prevention. These three umbrella communication themes were chosen because

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Twitter and H1N1

they shed light on the information stage portion of the innovation-decision process. Further analysis, through surveys and interviews, helped determine how Twitter users progressed through the next two stages of the process, persuasion and decision. While these Tweets can communicate information in multiple ways – among them humor – the three main themes were the most prevalent when the analysis was completed.

In this section, I begin by giving a broad view of the health-information-seeking behaviors apparent in Tweets that focused on three topics – symptom identification, preventative behaviors, and vaccination information. Next, I focus on misinformation and disinformation and the three topics that feed into this theme – deaths, preventative behaviors, and symptoms of the virus. Finally, I explore the theme of uncertainty reduction, and how information passed via Twitter on both preventative measures and vaccinations contributed to the information-gathering cycle individual users engaged in before entering the persuasion phase of the process, which was charted through the survey phase of this research project.

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Twitter and H1N1 Health-information-seeking Behaviors Health-information themes focus on “the origin, treatment, symptoms, and other

biological perspectives associated with a disease” (Wang, Smith, & Worawongs, 2010). By framing the H1N1 outbreak with medical or health information, communicators create a perception of the virus as a health crisis and lead others to understand the virus outbreak in this manner (Entman, 2007). Medical frames can allow communicators the ability to be more neutral in sharing information, and thus, researchers can conclude that more organizations would post health information-themed Tweets than would individuals (Clarke, 1991). According to Entman, medical frames can also “introduce or raise the salience or apparent importance of certain ideas, activating schemas that encourage target audiences to think, feel, and decide in a particular way” (2007, p. 164). In effect, Tweets that deal with health information and are framed in a medical manner help users in the first step of the innovation-decision process – the information stage. In my research, I discovered that Twitter users posted both information-seeking and -giving posts that fit into the health information theme. These Tweets point to the first step in the innovationdecision process – the information stage.

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Twitter and H1N1

Table 1 Tweets Relating to Information on H1N1 Categories of Information Health information Symptom identification Preventative measures

Vaccination

Misinformation and disinformation Symptom misidentification Preventative measure confusion Vaccination misinformation Uncertainty reduction Deaths Prevention of spread

Safety and availability of vaccines

Example Tweets Swine flu: symptoms so mild many donʼt recall them There is a lot more to preparing for and preventing Swine Flu than just washing your hands…

Swine flu ʻshould be included in new seasonal vaccineʼ – AFP

Swine flu publicity means uptick in OCD symptoms Human Protein That Can Prevent or CURE H1N1 Swine Flu—Naturally!

They are Injecting Mercury into Children Another swine flu death this time from Bahraich Ordinary disposable surgical masks do not protect health care workers from swine flu Swine flu jab receives good response

In addition, public health entities like the CDC use Twitter as a portion of their public health information network, which was instituted “to make communication easy, to make information accessible, and to make secure data exchange as swift and smooth as contemporary technology will allow” (Peddecord et al., 2008; Baker, Freide & Moulton, 1995). These public entities work to persuade users to embrace behavioral change, the true test of diffusion of an innovation, and this behavior was evident from some health information Tweets as well (Peddecord et al, 2008).

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Twitter and H1N1

Symptom Identification The first sub-theme relating to health information found during the content analysis of Tweets about H1N1 was symptom identification. It was a primary way users found both to seek and share information on the virus. The sharing of health information ranged from users telling their particular symptoms and seeking verification of those symptoms’ validity to complaining about other people’s symptoms when they appeared in public ill with what appeared to be the pandemic virus. A sample of symptom-related Tweets included: •

Feel crap. Reckon its #swineflu. How would ya know? A 2 wk cold that suddenly gets worse with fever and weepy eyes...feel like death

•

Swine flu: symptoms so mild many don’t recall them: http://url4.eu/1nYyy.CurAbility.10512376142.59884668.en

•

I am that coughing guy on the tube who you are looking at trying to determine if that's swine flu or just a nasty cold. #innocent #swineflu

•

100.4 fever... who wants to bet what time i end up at the ER? #swineflu

•

the fifth day the 2yrs old had fever hope it will stop tomorrow hate the #swineflu

•

sudden onset of extreme nausea & fever. #swineflu ??

•

Do you know the #symptoms of #H1N1 in #pets? http://is.gd/5pSyp #animals #cats #dogs #swineflu #flu #family

•

Dude in the office moaning and coughing like he has emphysema. #contagious #swineflu

•

On the train to london. Desperately trying not to sneeze on people and creating a #swineflu stampede. Still would get carriage to myself.

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Twitter and H1N1 •

In times of #swineflu it's somehow irritating to see people that handle your food cough or sneeze.

Tweets such as “Why would you go in public if you were non-stop coughing?!?” and “looking at people with disgust when they sneeze” summed up what many individuals felt about those who had either the regular flu or a more dangerous variant, yet refused to go to the doctor or stay away from crowds. While these tweets may appear that individuals on Twitter are simply complaining about inconsiderate sick people, perhaps their complaints were able to help other sick individuals from entering society and spreading their illness.

Individuals not only sought information about symptoms as shown above. Organizations were able to share pertinent information that would help individuals determine if they needed to seek medical advice because of sickness. Tweets like “What is H1N1 swine influenza & What are the symptoms?” followed with a link to a website containing more health information about the flu were able to connect many with concise and precise data about flu symptoms, treatments and prevention. Many health organizations were able to use the medium of Twitter not only to share information about vaccination clinics, as seen above, but also to help guide sick individuals to the proper treatment when necessary. In fact, the World Health Organization, one of the recognized global leaders in the fight against the spread of H1N1 was able to use tweets like “Swine Flu symptoms still widespread globally; statistics update by WHO” to share authoritative information with the approximately 75,000 followers who watch the @whonews Twitter feed to find out the latest information on H1N1 and other world health crises.

Information on Preventative Measures Symptoms were not the only subject that individuals and organizations sought and shared information about. Tweets such as “Morbid Obesity as a Risk Factor for Hospitalization and

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Twitter and H1N1

Death due to 2009 H1N1 Virus” led readers to more health information about the spread of the virus – combining data about the virus with more health information about preventative measures. The obesity-focused tweet was particularly popular for retweeting, or the passing along of information that individuals find to be important or particularly informative, as it was passed along the Twitter information superhighway another seven times – within the three days when Tweets were analyzed – beyond the initial sharing. The sharing of this pertinent information shows particular interest in how different lifestyle choices figured into the possibility of the virus’ spread. The obesity-centered H1N1 Tweet was but one example of how health information was tied to preventative measures – both for individuals seeking help and those trying to give help. The Tweets numbered in the hundreds in the dataset; here are a few examples: •

RT @fffabulous: simple preventative ways to avoid the swine flu #swineflu http://ow.ly/1kKOo (via @LoriGregory) #momspotting

•

There is alot more to preparing for and preventing Swine Flu than just washing your hands. .. http://tinyurl.com/ybxqq6d #swineflu

•

Household Transmission Of H1N1 Influenza During Initial Outbreak Limited By Preventive Behaviors http://mnt.to/3z4R #swineflu

•

New blog post: : Preventing Common Cold and Flu with an Air Purifier in Your Home http://bit.ly/cw7Zar #airpurifiers #cold #swineflu

•

#H1N1 #SwineFlu #News Surgical masks effective in preventing H1N1: http://url4.eu/21Cmt

•

#swineflu Clean Door Handles Prevent Swine Flu | quebella.net: If one of them had swine influenza you might have p... http://bit.ly/bnJrlD

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Twitter and H1N1 •

#swineflu H1N1 Swine Flu Prevention in the Dental Office | jellofart's blog: Personnel providing direct patient ca... http://bit.ly/ayV0G8

•

#SwineFlu Schools add Swine Flu Prevention 101 to their curriculum - WMBF http://ow.ly/1689K4

•

Do You Want To Keep Your Family Safe? Learn How To Prevent Swine Flu http://tinyurl.com/yl37894 #swineflu

•

#swineflu Alert swine influenza … You can prevent infection and a pandemic ...: When most people think about the p... http://bit.ly/a8Fguc

Examples such as the ones focused on sharing preventative measures within the classroom and the workplace are particularly strong illustrations of organizations utilizing the medium of Twitter to share health information with the world in order to help stop the spread of the virus.

H1N1 Vaccination Information Sharing Just as the swine flu Twitter stream featured hundreds of Tweets about both symptoms and preventative measures, posts focused – especially well into the outbreak – into sharing and searching for information on vaccinations. Information on H1N1 vaccination shared via Twitter is wide-ranging, from particulars on where and when flu shots would be offered to the possible side effects of H1N1 vaccinations. These health-information Tweets are focused on the information-sharing side of the equation, but one can also see a number of posts seeking information about the safety of vaccinations.

One of the most important uses of the medium during the height of the outbreak, however, was the use of Twitter to share information on vaccine clinics. Thousands of tweets such as “Free H1N1 vaccine available Sunday at Pensacola locations” allowed residents of certain areas the opportunity to find pertinent health information as well as alleviate uncertainty

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Twitter and H1N1

about the virus and its prevention. In addition, organizations whose job was to ensure that vaccines were administered were able to pass their message along in much the same way as they would use mass media. When individual Twitter users reposted information on clinics through retweets, the message from health organizations was simply communicated to a larger audience through the mass medium. Indeed, sharing information via Twitter on vaccination clinics became a new form of mass media, albeit with an interpersonal communication twist, and a valuable weapon for those fighting the spread of the virus. A sample of vaccination-related Tweets included: •

Swine flu vaccine producers reach last trial stage in India - fnbnews.com (http://cli.gs/uqb3r) #swineflu #H1N1

•

Commentary on potential CDC pandemic #H1N1 vaccine mismatches #swineflu http://bit.ly/9cMc9l

•

#SwineFlu #H1N1 #News Get your children vaccinated against swine flu: http://url4.eu/1no3F

•

Get your children #vaccinated against #swineflu - This Is Hampshire.net : http://bit.ly/aJOIJl

•

Contra Costa County Offering New #SwineFlu Clinics - CBS 5 : http://bit.ly/bK9K5q

•

#SwineFlu #H1N1 #News Scottish GPs hit swine flu vaccination targets: http://url4.eu/1o5kQ

•

RT @intouchwme RT @hniman: Comments on #H1N1 #vaccine failure in #Wyoming & D225G role #swineflu http://bit.ly/aX2o5S :[

•

Majority of 'at risk' Islanders did not bother with the #swineflu jab - Isle of Man Today : http://bit.ly/arZWuc

44

Twitter and H1N1 •

CSL Profit Beats Estimates on Swine Flu Vaccine Sales http://bit.ly/amWg5F #swineflu #vaccine

•

Swine flu 'should be included in new seasonal vaccine' - AFP (http://cli.gs/L6Njs) #swineflu #H1N1

Throughout the spread of the virus, individuals and organizations shared data on vaccination clinics as well as how the vaccine was perceived in different areas of the world. This information sharing was valuable to those who might not obtain information in traditional ways, particularly those who were in a high-risk category but unable to obtain information from traditional mass media or interpersonal connections.

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Twitter and H1N1 Misinformation and Disinformation While Twitter certainly allows legitimate organizations and individuals to pass along

information related to H1N1, its symptoms, prevention, and vaccinations, the medium also allows individuals and groups the opportunity to pass along false information. This data sharing is not always intentionally malignant. However, disinformation can negatively impact individuals’ ways of dealing with the virus during times of crisis. In addition, misinformation can cause individuals to avoid life-saving measures because there is a vacuum of correct information.

In the absence of data that could influence users to embrace the innovation of immunization or preventative measures, disinformation and misinformation can cause real issues for health-care providers. In short, the use of misinformation can prevent individuals from reaching the second step of the innovation-decision process, persuasion, which Rogers (2003) states is the stage in the process where an individual forms a positive or negative opinion of an innovation. In this case, individuals needed to form a positive opinion of the H1N1 vaccination and prevention techniques in order to practice them.

From uncertainty, which will be discussed in the following section, frequently comes a spread of misinformation. A lack of information in a crisis creates a vacuum that communicators are driven to fill (Ulmer et al., 2007). For instance, during the October 26, 2009, shooting on the University of Central Arkansas campus, individuals were driven to blogs, Twitter, and other CMC tools in order to find out more information. The quickest individuals to respond to uncertainty with information are frequently not the ones with correct information. The spread of misinformation could easily be seen in the beginning of the H1N1 outbreak by the numerous mentions of staying away from pork or avoiding travel to certain areas of the world – both Tweets that were carried around the world but that had no actual basis in fact. In this case,

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however, unlike scholars had earlier noted, misinformation and disinformation were not passed along because of an ill intent or a desire to persuade (Hatchen & Scotton, 2007). Instead, individuals were trying to fill an overwhelming information vacuum with their Tweets.

Symptom (Mis)identification Perhaps the most misinformation on H1N1 that was shared via Twitter concerned the identification of symptoms related to the spread of the virus. According to the Centers for Disease Control and Prevention (2010a), H1N1 flu symptoms included fever, cough, sore throat, runny or stuffy nose, body aches, headache, chills and fatigue. However, during the height of the virus discussion, Tweeters attributed everything from hot hands to pale skin to the onset of the virus. No doubt many individuals were confused if they were unable to verify information about symptoms of the virus with expert sources. Posts that featured misinformation about H1N1 symptoms differed from posts that featured health information-seeking and -sharing because the individuals who broadcast symptom misinformation listed incorrect symptoms. In addition, these Tweets with misinformation focused on missing work or school or taking advantage of one’s apparent symptoms.

In the course of the three days targeted for content analysis, the tweet “I look like a #zombie really feel awfull, … maybe i have the #swineflu” appeared over 120 times in the analyzed tweets. The individual user who originally sent this tweet may have known that H1N1 symptoms include fever, aches, and fatigue, but he or she may not have known that a simple complaint would spread so rapidly (CDC, 2009a). And while this Twitter user did not spread completely false information, his or her approach certainly raised red flags for those of her followers who were concerned about the spread of the disease and their contact with him or her.

Individuals also expressed concern when actress Lindsey Lohan tweeted about being achy

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Twitter and H1N1

(“Lindsay’s tweet sparks,” 2010). Her offhand remark sent shockwaves through news media as well as the Twitter universe, with a number of tweets such as “Lohan sparks #swineflu fears with ‘achey’ tweet” being sent after her original message. Other Tweets that spread misinformation about symptoms included: •

Swine flu publicity means uptick in OCD symptoms - Gloucester Daily Times: All those swine flu warnin.. http://bit.ly/7IzOUl #swineflu

•

I'm almost certain that with the amount of coughin sneezing and blowing of the nose going on in this train car somebody has the #swineflu

•

Speaking at Harvard: - Salon: randomly shouting -Swine Flu- at anyone who coughed.) I experienced my .. http://bit.ly/7asewC #swineflu

•

Going down hill rapidly I hope this isn't swineflu #swineflu symptoms

•

#nevertrust a person that says “my allergies actin up” a cough a sneeze or a runny nose lasting more than 3 days = #swineflu

•

#nevertrust someone thats been coughing/sneezing and wants to give u daps or if its a female give u a hug...#swineflu

•

#nevertrust a person that's too touchy feely especially if you don't them #swineflu #H1N1

•

S/O 2 Me Coughing all over the place acting like I got the #SwineFlu ... Lol can we say day off #WithPAY Hello Brooklyn :-) !!!!

Confusion on Preventative Measures Alongside many Tweets about symptoms that contained misinformation and disinformation, there were a large number of Tweets that featured dubious data on preventative measures. Most of the tweets aimed at preventative measures appeared to be designed to sell

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Twitter and H1N1

some specific tool or information about the spread of the virus. And for almost every one tweet that tried to sell something, there was a tweet designed to debunk the misinformation that was passed around. For instance, for every “Should I wear a flu mask to protect myself from swine flu?” tweet accompanied by a link to a medical supplies warehouse, there was a “Garlic Sellers Cashing In On Flu Rumors (That Garlic Prevents Swine Flu” to dispel myths. In this way, Twitter became a self-correcting network during the H1N1 outbreak. Individuals were able to find information – both correct and incorrect – about the virus, and they were able to react appropriately when choosing preventative measures. A few of the preventative measure Tweets were: •

Preventing Illness- Including the Flu! | www.healthyindoorairllc.com http://ow.ly/196RZ #h1n1 #health #swineflu #flu

•

#SwineFlu Human Protein That Can Prevent or CURE H1N1 Swine Flu--Naturally! Examiner.com http://ow.ly/16b9Kp

•

Aurelie with the bear mask : http://bit.ly/7yDqLA #mask #swineflu #bear #H1N1 #vaccine #flu #protection #pimp #prevent

•

Tonight I'm trying a humidifier some voo-doo and any other magical snake oil I can find. #bedtimesucks with my cough from the #swineflu!

•

Epigallocatechin Gallate (EGCg) in Green Tea Confirmed to Prevent ... - Yahoo Finance: Influenza viru.. http://bit.ly/8TCwY1 #swineflu

•

#SwineFlu Prevention Tip 34: This Christmas don't open any presents and avoid all contact with loved ones to keep from getting sick.

Tweets aimed at helping individuals prevent the spread of the swine flu virus ranged from

the innocuous “Here are 10 swine flu prevention tips” to the inflammatory “Think schools should

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Twitter and H1N1

be closed to prevent #SwineFlu outbreaks?” While the first tweet was clearly informationsending and designed to help individuals, the second tweet on prevention was informationseeking and appeared to be designed to spark a discussion on the virus. Especially during the height of the virus spread, such discussions could quickly go awry, resulting in arguments over the safety of everything in the brave, new world that contained such horrors as H1N1.

Vaccination (Mis)information While organizations and individuals worked to share the correct and official information concerning vaccination programs surrounding the H1N1 outbreak, a great deal of what could be perceived as misinformation was also shared about vaccines and their safety. These posts – ones that shared incorrect information or showed that the user was uninformed about the vaccine – disagreed with public health policy that advocated the vaccination of most individuals (Centers for Disease Control and Prevention, 2009b). Side effects resulting from a vaccination were the most common types of posts that featured misinformation. These misinformative vaccination posts were different from other Tweets focused on vaccinations because they listed a host of negative symptoms that were different from those listed in CDC materials. The CDC listed the most common vaccination side effects as soreness, redness, and swelling where the shot was given (2009b). However, Twitter users listed a litany of possible negative effects from the vaccination as well as questions about the vaccinations’ efficacy and safety; a sample of those Tweets included: •

My arm hurts and I feel weak and milky today. Poor me. #symptomsfromtheswinefluvaccine #swineflu

•

If these are swineflu #vaccine symptoms I do not want to have the #swineflu.

•

#Swineflu jabs may be wasted - The Age : http://bit.ly/atbTFG

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Twitter and H1N1 •

Live #Radio #Today 3PM EST They Are Injecting #Mercury into Children http://bit.ly/7xot3i #H1N1 #Swineflu #novacs @mayereisenstein

•

#SwineFlu Severity warning over low uptake of swine flu jab - The Standard http://ow.ly/169yeF

•

Vulnerable patients shunning #swineflu #vaccine GPs warn - Telegraph.co.uk : http://bit.ly/44yKGA

•

No soreness in my arm but am feeling the first side-effect of the vaccine: lethargy #swineflu

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Twitter and H1N1 Uncertainty Reduction In addition to communicating via Twitter on H1N1 for health information and finding

information, misinformation, and disinformation, users of the microblogging service were able to use Twitter to alleviate uncertainty. Frequently, information was shared to dissuade individuals from believing the misinformation and disinformation that were passed along through Twitter at the beginning of the outbreak. When Tweets were examined for content, a large number surfaced that related to the uncertainty that individuals felt in the context of a worldwide health crisis.

Ulmer, Sellnow, and Seeger (2007) define a crisis as an exceptional event, something that results in a certain amount of surprise and threat for individuals as well as a situation that requires a short response time for communication of answers or assistance. In fact, the less individuals know about a given situation, the more uncertain they are and the more they search out appropriate information to make themselves comfortable with a situation (Littlejohn & Foss, 2008).

The H1N1 outbreak in 2009 is a pertinent example of a health crisis; a large amount of uncertainty resulted for individuals who felt they were in danger of contracting the virus. The unique pattern of deaths that resulted from the virus (Figure 2), with a large number of young adults dying from the disease, caused even more uncertainty for those who would normally consider themselves safe from such an infection. As a result, a large number of Tweets were posted that showed individuals’ concerns with deaths as well as a number of Tweets that were focused on finding information about symptoms and vaccination sites. Though these sub-themes repeat the themes discussed in earlier sections, an analysis helped discern that individual users and organizations are clearly concerned with alleviating uncertainty through CMC. As a secondary result of the effective use of Twitter to remove uncertainty, organizations were able to

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Twitter and H1N1

bring individuals to the tipping point of information, helping them enter the persuasion phase of the innovation-decision process (Figure 6).

Figure 6. Representation of the tweeting processes Involved in persuasion for decision making in H1N1 vaccination. Adapted from Ev Rogersʼ Diffusion of Innovations.

Information about Deaths On each of the three key analysis dates, a number of Tweets appear detailing death counts in various countries, and these “death Tweets” are directly tied to the uncertainty-reduction theme. While individuals may not like hearing about the number of deaths throughout the world because of H1N1, it was certainly better for users to know the truth than guess about the possibilities. Observers were able to watch the spread of the virus throughout the world via Twitter. In addition to sharing information on numbers and the virus’ spread, these focused

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Tweets allow a view into the effect of the virus on health-care workers. For instance, a tweet stating “Moldova: 15 H1N1 deaths incl doctor infected from patient” reminds observers that those who are entrusted with treating patients are also in danger of contracting H1N1. Other “death Tweets” included: •

#SwineFlu Another swine flu death this time from Bahraich - Indian Express http://ow.ly/16fSWn

•

CDC Offers Latest Estimates of H1N1 Toll http://bit.ly/9It0wL #h1n1 #swineflu (via @Breaking_h1n1)

•

Medics meet to discuss #swineflu death - Daily Echo : http://bit.ly/apIpWu

•

US #h1n1 #swineflu figures for last year: up to 86m infections 12000 deaths http://is.gd/aJtN2

•

Swine Flu Death Toll in India goes up to 1415 - BreakingNewsOnline. (http://cli.gs/Tyq7L) #swineflu #H1N1

•

2 some-more deaths in Oklahoma uncover H1N1-flu risk remains http://tinyurl.com/ybfuvvk #swineflu #hongkong

•

Global #swineflu death toll creeps towards 16000: #WHO - Victoria Times Colonist : http://bit.ly/b8R7L6

Spread of H1N1 Prevention Information In addition to sharing H1N1 death tolls or seeking information on them, individuals who used the social media microblogging service sought and shared information on their symptoms.

Organizations also used the medium to share data about how to prevent the spread of the virus.

These tweets went much further than the health information recommendations for hand washing and covering the nose and mouth when coughing and sneezing, however. Many groups –

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companies that were probably marketing items designed to play on individual fear about H1N1 spread – used the service to tout their wares as the best way to keep from getting swine flu. By using Twitter in this way, these companies not only shared health information, they also took advantage of individuals’ uncertainty about the H1N1 virus and its spread. Examples of these Tweets designed to relieve uncertainty about prevention of the virus include: •

#swineflu Swine Flu- How Can I Optimize My Immune System? | Swine Flu: Swine flu or swine influenza was first http://url4.eu/1nLT0

•

Swine Flu Protection. Flu Masks Surface Disinfectants. Be prepared. Protect yourself today. http://tinyurl.com/ydocc6f #swineflu

•

Ordinary disposable surgical masks do not protect health care workers from swine flu. http://tinyurl.com/yco97jd #swineflu

•

Study links lack of paid sick days to spread of Swine Flu - Bristol Press (http://cli.gs/sa5Xy) #swineflu #H1N1

•

N95 masks are the only masks that provide protection from the swine flu virus. http://tinyurl.com/y9sruou #swineflu

•

Swine Flu: Know what to do if a family member gets sick http://tinyurl.com/ycewerh #swineflu

Safety and Availability of Vaccines Finally, individuals and groups used the medium of Twitter to communicate information about vaccinations – their safety and availability – in order to alleviate uncertainty within the general population. The vaccine talk received much attention in each of the areas covered by H1N1 themes in this content analysis. A sample of the information-seeking and -supplying Tweets that focused on vaccinations are:

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Twitter and H1N1 •

#SwineFlu #H1N1 #News Swine flu jab receives good response: http://url4.eu/1nPLY

•

Commentary on potential CDC pandemic #H1N1 vaccine mismatches #swineflu http://bit.ly/9cMc9l

•

U.S. may end up discarding unused #swineflu vaccine http://tinyurl.com/yk9b97b #tcot #tlot

•

#H1N1 #SwineFlu #News 500000 people vaccinated against A/H1N1 flu in Mexican capital: http://url4.eu/1QdPl

•

#H1N1 #SwineFlu #News 34300 doses of imported H1N1 vaccine arrive in Mumbai: http://url4.eu/1p07Z

•

#H1N1 #SwineFlu #News Azerbaijani health minister: No complications in A/H1N1 vaccination in Azerbaijan: http://url4.eu/1RH3R

The concept of using information to promote particular health-related behaviors is not a new one, though scholars have recently become more interested in health information as an area for study. The use of entertainment media in particular has become more popular in recent years, as scientists and doctors attempt to use friendly methods of sharing health information with the individuals who are most affected by particular diseases and syndromes (Peddecord et al., 2008; Singhal, Njogu, Bouman, & Elias, 2006). From birth control to preventing HIV and AIDS to encouraging vaccinations, communicators frequently use a variety of mass media and entertainment venues to share information with audiences.

The use of Twitter to talk about the H1N1 outbreak is no different. In fact, according to a post from the computer-mediated communication tool, “#swineflu and #H1N1 were two of the most popular hashtags in all of 2009. #whenswineflew.” Twitter can also help individuals move

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from the first step of the innovation-decision process – information acquisition – to the second step – persuasion. In the second section of my research, I worked to discern how much individual Twitter users paid attention to their feeds when it came to information on H1N1.

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Twitter and H1N1 Results – Surveying the Users Following the content analysis of Tweets about the H1N1 outbreak, an online survey was

conducted to find out basic information about Twitter users’ social media habits as well as the amount of influence that Twitter has on their off-line behaviors. Some general demographic information from Arbitron-Edison Research on Twitter users in 2010 is helpful for understanding how individuals use the communication tool (See Table 2). Overall, more women (53 percent) than men (47 percent) use the tool. A majority of users are white (51 percent), followed by black (24 percent), Hispanic (17 percent), and Asian (3 percent). A third of users fall in the 25-34 year old age range, while the next largest groups are 35-44 year olds (19 percent) and 12-17 year olds (18 percent).

The average Twitter user fits a demographic profile that conforms to Rogers’ definition of opinion leadership. These individuals are exposed to more mass media, are involved with more groups than their immediate social group, are more innovative, and are accessible to many individuals – sharing their opinions with many (Rogers, 2003). According to Arbitron surveys, individuals who utilize the microblogging service fit this description well. More Twitter users (47 percent) record an average household income over $50,000 per year as compared to the total population (33 percent with incomes over $50,000 per year) (Edison Research, 2010). Twitter users are well-educated, with 63 percent of the population having a four-year college degree or more. Only 40 percent of the general population has a college degree or higher (Edison Research, 2010). These users are also regular cell phone and computer users, and they spend more time on social networking that those who choose not to use Twitter (Saint, 2010). In addition, forty percent of Twitter users have three or more computers in their homes (Saint, 2010).

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Table 2 Portrait of Twitter Users Whoʼs Using Twitter Sex Men

47%

Women

53%

Race White (Non-Hispanic)

51%

Black (Non-Hispanic)

24%

Hispanic

17%

Asian

3%

Other

5%

Age 12-17

18%

18-24

11%

25-34

33%

35-44

19%

45-54

12%

55+

7%

Source: Edison Research According to Rubin, Rubin, and Piele (2005), survey research is counted as people- or behavior-oriented research, which centers on the actions and reactions of individuals and can include “self-report of attitudes and behaviors via survey questionnaires, observations of other

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people’s behavior, and experimental research” (p. 226). Survey research in particular works to establish a link between attitudes and opinions and events, and the present survey responses were analyzed using descriptive research methods (Rubin et al., 2005). The present survey focuses on connecting individual users’ attitudes about the virus pandemic with their behaviors, specifically concerning accepting or rejecting vaccinations. These behaviors demonstrate the decision stage – step three – in Rogers’ innovation-decision process. In stage one of the process, individuals learn more about a certain innovation, while in stage two, prospective users are persuaded to accept or reject the innovation (Rogers, 2003).

Step three shows the part of the process where individuals “engage in activities that lead to a choice to adopt or reject an innovation” (Rogers, 2003, p. 177). In this stage (Figure 7), rejection is just as likely as acceptance, and the rejection can be either active or passive depending on whether or not an individual actually considers the innovation for possible acceptance (Rogers, 2003). In this study, several steps within the decision phase of the innovation-decision process were found. In order to accept or reject the innovation of an H1N1 vaccination, individuals will receive information via Twitter in the form of interpersonal chat and mass media, and this information will be combined with persuasive talk (interpersonal communication) with others in their social networks. The data contained in both the Twitter postings and talk with others in a social network combines to help individuals make their own decisions about vaccinations.

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Figure 7. Representation of the tweeting processes Involved in the decision stage for H1N1 vaccination. Adapted from Ev Rogersʼ Diffusion of Innovations.

As mentioned earlier, an invitation to complete an online Twitter survey, which is “an efficient means of gathering data from large numbers of people” (Rubin et al., 2005, p. 227), was circulated. In response to the invitation, 42 individuals responded to every question on the questionnaire. These individuals were spread across the country and of all age ranges, but they had one thing in common – an interest in helping determine the importance of Twitter as a communication medium. In this section of the paper, I will first discuss the results of this survey in relation to an individual Twitter user’s basic behavior online. This information helps one determine how active a user has been and how long he or she has been engaged in using the medium to pass information along to followers and to learn from other users. Then, I will focus

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on individual user’s self-reported behavior in relation to Twitter and communicating about the H1N1 pandemic flu. Finally, in this section, I will examine the self-reported decisions that Twitter users made about seeking vaccinations for themselves or their families. The analysis also provides information from a series of short follow-up interviews with respondents to the original survey.

Individual Use of Twitter The first section of the Twitter user survey focused on allowing individual Twitter users to report basic information about their daily use of the social media platform. These questions focused on time of engagement, regularity of use, and the decision-making process on sharing information. There were both open- and closed-ended questions in this section of the survey, and each of these questions was designed to ascertain the daily experience of being a Twitter user.

Individuals who completed the survey used Twitter from a month to almost three years.

The greatest percentage of respondents had maintained a Twitter account for approximately a year – 14 or 33 percent. The range of followers each respondent has was wide as well – going from 2500 at the highest to 15 at the lowest. The average number of followers for respondents was 393.5, meaning that each of the survey respondents communicated regularly about a variety of topics with close to 400 individuals.

Considering the larger number of followers, each of these Tweeters could be considered an opinion leader for those followers, and they could share much information with their interpersonal network. Users, however, stated that they were often conservative with the amount of information they shared with the public. A majority of users sent only one to three tweets per day, while the second highest percentage of users communicated via Twitter more than 10 times per day (See Figure 8). Thirteen respondents said they shared information via Twitter more than

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10 times per day, while 14 said they Tweeted one to three times per day. Twelve respondents said they shared information four to six times per day, and five said they posted seven to nine times per day. The number of times that an individual user issues Tweets or posts to the Twitter application throughout the day denotes how active an individual is in the medium. For instance, if an individual Tweets three times per day, that would mean he or she sends messages approximately every eight hours. However, if a user posts 10 or more times per day, that person is heavily involved in the medium. His or her posting rate is once every two hours at least, and chances are that that user places a great deal of value on the information he or she finds on Twitter. Exposing oneself to an idea – and perceiving it as positive – is connected with placing value on an innovation and its eventual adoption (Rogers, 2003).

Tweets per Day

10 or more

7 to 9

4 to 6

1 to 3

0

2

4

6

8

10

12

Respondents

Figure 8. Representation of Twitter use per day by survey respondents.

14

16

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Twitter and H1N1 Users were then asked about their propensity to pass along information seen on Twitter,

the process known as “retweeting” (PCmag.com, 2010). This question was followed with an open-ended question asking about users’ reasons for deciding to retweet information. Because individuals expose themselves to information that tends to work in accordance with their established needs, one can assume that one passes along information for that same reason (Rogers, 2003). As a result, users would retweet information that they not only have a particular interest in, but that they also agree with. In Figure 9, one can see that the majority of survey respondents (about 27) retweet information one to three times per day. Ten Twitter users said they retweeted information four to six times per day, and three individuals said they retweeted information seven to nine times per day. Only two respondents said they reposted pertinent information more than 10 times per day. The limited number of retweets indicates that individuals are selective about the information they choose to pass along to their followers on Twitter. This selectivity leads to the next survey question, an open-ended one focused on how individuals determined which information they wanted to pass along via retweeting.

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Retweets per day

10 or more

7 to 9

4 to 6

1 to 3

0

5

10

15

20

25

30

Respondents

Figure 9. Representation of the retweets per day by survey respondents.

When asked how they determined which information to pass along, individual users gave a variety of answers. A common theme in these responses, however, was the attention to the information that individual users placed value in and that they thought their followers would place value in. One respondent said he passed along information that was “important to myself and my followers,” while another said he shared a previous Tweet if “there is a strong relationship with the person tweeting and the tweet is important to him/her.” Another respondent noted “if something is funny and I think my friends would also enjoy it, I retweet. If something is useful to me in some way, I retweet.” Yet another said he shared “Opinions I agree with that are especially well spoken. Something that promotes something I am interest in or participate in.

Something of interest that comes from someone who I know isn't in my circle of followers.” This individual in particular noted that his diverse set of followers might not be linked to one another on Twitter, so his presence was the connective tissue that brought different worlds together. One

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Twitter user summed up the majority of respondents’ answers in saying that in order to pass the “retweet test,” information had to answer the question “Is it VALUABLE information that I want to share with my followers?” in the affirmative.

In these responses, the words “common,” “important,” and “valuable” echo through many individual answers to this particular survey question. These statements reiterate Rogers’ (2003) premise that during the information stage of the innovation-decision process, individuals are searching for information that will help them make decisions on adopting or rejecting an innovation, but they want this information to correlate with their preconceived notion on the innovation that they are researching.

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H1N1 Information on Twitter The next section of the Twitter user survey focused on an individual user’s attention to information pertaining to the virus outbreak. This question was designed to determine when an individual first heard either health information, misinformation, or information designed to reduce uncertainty – the three themes found in individual tweets during the content analysis stage of this research – about the H1N1 pandemic flu. Surprisingly, a majority – 24 – of the 44 users who answered this question could not remember when they first heard information about the virus. Perhaps that was because there was a large lag time between the time that the virus first emerged, in the spring of 2009, to the time when the survey was administered, which was in March 2010. The salience, or relevance, of the issue of an H1N1 outbreak was not at its height in early 2010 – the virus had largely run its course – so perhaps Twitter users found another crisis to occupy their memories and their time. Among the other answers to this question were 16 different dates ranging from early spring 2009 to late fall 2009. The latest date given by a respondent was two months before the March 2010 response date on the survey, and one individual noted that he heard about the virus on Twitter at the same time as he heard about H1N1 on the news.

Although most survey respondents could not remember the exact date when they heard information about the H1N1 virus on Twitter, a majority of respondents were able to identify precise categories of information they heard about the outbreak while reading posts. Many of the responses to this open-ended question were divided among the three major themes found in the content analysis section of the Tweet research (See Figure 10).

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9 None/Don't remember

4

Uncertainty Reduction

Health Information Health Information

19

Misinformation Uncertainty Reduction None/Don't remember

Misinformation

6

Figure 10. Representation of the information seen about H1N1 on Twitter by survey respondents.

Surprisingly, many of the respondents remembered H1N1 Tweets that related to specific misinformation about the virus. For instance, one user noted that there were “lots of bad pig jokes,” while another remembered making many of those bad jokes. Still another user recalled “crazy rumors about it. People being worried about their kids not being eligible for the shot.

Some people trying to convince others to not give their kids the shots.” Another user listed the topics he remembered seeing on Twitter in regard to the H1N1 outbreak. These were: •

deaths due to virus

•

tips for prevention

•

info related to contamination

•

info related to vaccine. (positive and negative)

•

daily info on where the virus was and direction it was spreading

•

info on how to handle the virus in the work place

•

info on how to handle the virus in schools

•

statistics

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Twitter and H1N1

This respondent’s list of Twitter-H1N1 topics closely paralleled the themes discovered during the content analysis conducted earlier in research, specifically the health communication and uncertainty reduction themes.

After asking what information individual users recalled from their Twitter feeds that had to do with the H1N1 virus, the survey asked users how they verified the truth of the information they had seen concerning the outbreak. Surprisingly, of the 36 responses to this question, a third of respondents said they did not verify information they saw on Twitter. Since this question was open-ended, individuals had the chance to reveal more information about their choice to verify or not. Some of those who did not verify information said their choices were made because “I feel that the people I follow on tweeter are credible resources of information - as they are professional folks whose reputations are tied to what they say in this forum.” Other users said they trust information when it comes from government or health-related sources, like the CDC.

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Twitter and H1N1 Results – Implementation Stage of H1N1 Information The final questions on the Twitter user survey focused on how many individuals chose to

vaccinate against the H1N1 pandemic virus and then the information these individuals used to make their decision to adopt or reject the innovation and implement that decision. According to the CDC, 61 million people, or 20.3 percent of the United States’ population, received H1N1 preventative vaccinations (Centers for Disease Control and Prevention, 2010b). Of the 44 respondents to the question “Did you have an H1N1 shot this year?” only 11 reported that they had received the vaccination that was advocated so heavily. Only one-fourth of respondents entered the implementation stage of the innovation-decision process and put the innovation to use – displaying an “overt behavior change” (Rogers, 2003, p. 179).

The next question in the survey was open-ended and asked respondents if they saw anything on Twitter that helped them reach the implementation stage of the innovation-decision process. Thirty-six responses to this question were recorded, and a number of reasons were given for individuals’ choosing to enter the implementation stage. Many of these reasons were not directly tied to information these respondents saw on Twitter. Several respondents noted that information was pushed at them from several directions, persuading them quite effectively to seek out vaccinations to ward against an H1N1 infection. However, some Twitter users also noted that the information they gleaned from the medium did not ultimately persuade them to vaccinate. Instead, they relied on information from “close friends or people I follow getting vaccinated” to help them consider the inoculation. Still others noted that Twitter did help them decide to get the shot, saying “Information from Twitter convinced me that getting the shot would be a good idea, but I never did,” or that they used the information provided through posts “to search for dates and times the vaccination would be administered.” Many individuals who

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got the H1N1 vaccination attributed their decision to other factors than the information they obtained from Twitter. Individuals who chose not to adopt the vaccination innovation also attributed their behavior to more than the comments and information posted on the microblogging site.

In order to obtain more information on the implementation stage of the innovationdecision process in relation to H1N1, a follow-up survey was conducted with 10 of the original respondents to the Twitter survey. These individuals volunteered after I solicited via Twitter for individual users who would like to give more information about their experience with Twitter and H1N1. Specifically, the respondents were asked again if Twitter helped them decide to vaccinate or not to vaccinate, and then what other information contributed to their decision. Each of these individuals listed other information sources they consulted in order to come to their decisions about the H1N1 vaccination.

Again, these individual users noted that they found information on vaccinations from a variety of sources, including Twitter. Ultimately, however, their decision was made because of the addition of external circumstances instead of simply because of information found on the microblogging service. One interviewee said she obtained the shot simply because she couldn’t “afford to get sick for time or money. There were a lot of external circumstances that made me get the shots.” Still another respondent said he obtained a vaccination because he was around a high-risk group and wanted to ensure his family was safer from infection. Several of those interviewed in this segment of the research said they were unable to get a vaccination because they were not in a high-risk group. The most telling information, however, came from the group of individuals who chose not to get vaccinated against H1N1. One respondent noted “My brother is a registered nurse. He wasn’t too keen on it and had heard so many iffy things about it. I don’t

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come into a lot of contact with it at work, and I wasn’t in one of the risk groups.” Still another user noted her aggravation with the amount of emotion that the virus outbreak stirred in individuals. She said, I don't fall into the major at-risk groups for flu at this point, and I dislike the overblown media hype surrounding these things. We live in a ‘fear everything’ society, and I get so sick of it. The numbers didn't show any significant danger, just a new strain hitting the same targets. Flu is flu, and if the ‘superflu’ comes it's unlikely to really matter if we get a shot.

Again, the majority of individuals who responded to this question did not obtain the H1N1 vaccination. The lack of implementation – whether or not individuals’ decisions were tied to information they obtained through Twitter – certainly has interesting implications for how health agencies communicate with people about health risks and crises in the future. In the next section of this paper, I will discuss how this research has practical implications for health communication professionals as well as those involved in persuading others to accept innovations.

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Twitter and H1N1 Implications This research project has a goal of providing a view of the new communication medium

Twitter as a vehicle for information about the H1N1 pandemic flu. As a secondary goal, the project aimed to see how the innovation-decision process progressed with the help of Twitter and how individuals were seen as opinion leaders within their social networks through Twitter. This data helps us determine how best to use Twitter as an information-sharing vehicle that can assist organizations and individuals to communicate data designed to persuade the masses to adopt positive health behaviors.

In the current section of the paper, I will discuss implications for the results of the three sections of my research. Focusing on the content analysis, Twitter-user survey and follow-up interviews, I will discuss how Twitter is used to share information on crises, how users should verify this information delivered via Twitter, and how organizations should encourage Twitter opinion leaders to share pertinent health information and persuade others to adopt better health behaviors. I conclude with suggestions for practical application of the research and for future research in the area of computer-mediated communication.

Twitter as a Health-Information-Sharing Tool The content analysis section of this paper was designed to answer research question 1 – How is Twitter used as a communication channel for H1N1 information diffusion? In the first stage of Rogers’ (2003) innovation-decision process, prospective users learn about a particular innovation. Individiduals do not begin to develop an opinion on the idea or innovation; they simply learn more data in order to prepare themselves for the second stage – persuasion. Through the content analysis, I learned that individual users sent a wealth of information through the communication channel. In addition, individuals and organizations used the channel to help

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others develop a positive or negative opinion of vaccinations, the innovation that was available for adoption (Rogers, 2003). In order to make the most effective use of Twitter as a communication tool, individuals and organizations should first examine which information is salient to news consumers and then respond to those results with data that fits the needs of consumers.

Twitter was used to share much information on H1N1, but three major themes emerged through the content analysis – health information seeking and sending, misinformation, and uncertainty reduction. Individuals and organizations used these three themes to manage a majority of their communication about the health crisis, and each of the Tweets issued helped individuals either accept or reject the innovation of flu vaccinations. Because of the magnitude of data – 300,000 Tweets over the time period when H1N1 was in the news – shared with the new communication channel, people obviously thought the medium was ripe for sharing information related to a salient event. The chief concern for organizations and individuals must be if Twitter is an effective use of time and resources in order to communicate important information on health crises.

Obviously, individuals and organizations now use Twitter to communicate more and more – both as a mass medium and an interpersonal channel. A majority of survey respondents said they Tweeted on average one to three times per day. However, a substantial number (13) reported that they Tweeted 10 or more times per day. Those who use Twitter at this level pass along quite a bit of information to their followers, and those listeners have become accustomed to seeing information that is important to these superusers passed along through computermediated communication. In addition, a large number of users (27) retweet information at a rate of one to three times per day. Because of the frequency of Tweets – and the concentration of

74

 

Twitter and H1N1

Tweets about certain subjects – organizations wishing to use this medium to communicate pertinent health information should share more data about those important subjects. By posting more Tweets about health information and crises, organizations will draw more attention for their innovations such as vaccination against the H1N1 virus.

As one can see by the rate of information shared along the Twitter superhighway, individuals and organizations cannot ignore the importance of this medium. More and more individuals pay closer attention to the information they receive through Twitter, and they often choose not to verify the information passed through this channel. Organizations should work to harness the power of the social network created through Twitter by opening their own Twitter accounts and connecting with users who then have the power to share information on their behalf. If these organizations do not provide positive and correct information for their followers or opinion leaders to share, then these individuals will share negative or incorrect information.

Twitter users will simply be filling the information vacuum with what data they can find. In effect, organizations that wish to affect health behavioral changes should work with prospective opinion leaders in order to harness their power in the medium of Twitter to persuade others to adopt or reject health innovations.

Because so much information is shared through Twitter, organizations cannot afford to ignore the possibilities for communication with it. Unfortunately, however, some of the information passed through Twitter regarding the H1N1 virus was incorrect. This misinformation caused great amounts of uncertainty for users, forcing individuals and organizations to resort to sharing even more information to reduce uncertainty in relation to health behaviors such as H1N1 prevention and vaccination safety.

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Twitter and H1N1

Correcting Misinformation via Twitter Individuals and organizations that face misinformation on health crises on Twitter should employ three tactics to dissuade others from believing incorrect data when they find it. First, organizations should work to combat the misinformation that happens to be shared by giving correct information – with the means for individual users to verify that data through links to independent sources – through the medium as well. Second, individual users must verify the information they find before passing it along through the retweeting process. Finally, users should employ the self-governing method inherent in blogs and other computer-mediated communication of calling attention to incorrect information and then correcting it for future readers.

As found in the content analysis, Twitter users focused their H1N1 Tweets in three major themes, which were then divided into sub-themes – vaccinations, deaths, symptom identification, and prevention. These themes and subthemes conformed exactly to the information and persuasion stages in Rogers’ innovation-decision process (2003). Because these sub-themes were tied to both correct information and misinformation, those change agents charged with persuading individuals to adopt better health behaviors should pay close attention to what type of information is shared through this channel. No longer can health organizations stand by and watch as misinformation is shared through Twitter. There are too many users, and, as seen in the survey portion of the research, these users inherently trust the information they receive through Twitter perhaps because they choose the individuals to whom they pay attention. If misinformation is shared, therefore, it can spread like wildfire through the new medium because of the trust and strong social networks developed through Twitter.

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Twitter and H1N1 Research question 2 focuses on how individuals verify information on Twitter – how

credibility can be determined through the channel – and how this information influences their behavior. Users were asked if they verified information on Twitter, and a third of respondents to the web-based survey said they did not verify information they found on Twitter – either because they trusted the source or they knew the individual personally. This fact brings home the point that individuals who use Twitter to communicate important information should give their followers ways to verify the information they are passing along or they should work to ensure that they validate data before sharing it with the masses.

These results also reinforce that individuals who follow others on Twitter see those users as opinion leaders in certain areas. For instance, an individual who posts directly from the line at a vaccination clinic will be seen as an expert on the availability and wait for vaccines in a particular area. In addition to stating that they trusted individuals they considered opinion leaders on Twitter, users related in the survey that they placed emphasis on health-information Tweets from organizations like the CDC or news organizations because those groups inspired trust no matter which medium they used to communicate. Once again, these results show that individuals and organizations must ensure that the data they pass along through Twitter is of the highest quality. The salience of information relating to health crises is too high for individuals to be allowed to pass along misinformation or disinformation that could mislead others.

Salience of information involved in health crises also brings home the fact that Twitter users must govern themselves and others when misinformation is passed along. Individuals, especially those who are disengaged from interpersonal networks that might help them evaluate information they see on mass media like Twitter, rely on the data from mass communication.

Therefore, Twitter users must feel a personal, ethical obligation to correct misinformation when

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they find it. By correcting information in a public setting like Twitter, users validate their followers’ belief in their credibility and the authenticity of future information they may pass along.

The responsibility for sharing correct information on Twitter lies not only with organizations, change agents, and their approved opinion leaders. Individual Twitter users should also verify information shared through the medium before they blindly forward it through CMC or communicate it through other channels. When individuals pay close attention to their Twitter streams and verify the information dispersed there, hopefully they will be able to separate the wheat from the chaff. They can then act on the correct information presented there instead of acting – or waiting to act – on misinformation.

The presentation of both types of information side by side – in the way that Twitter presents information in a stream – allows individuals to compare and contrast, verify and refute information and determine the most prudent course of action for their individual situation.

Hopefully, the communication style used in Twitter, which presents all information and allows users to determine which data is correct or incorrect, will help create conscientious users of information. More seasoned users realize that others can pass on information without verifying it, so they are able to regard it with a grain of salt. Twitter users should therefore be encouraged to be shrewd consumers of information delivered via the medium. They should be educated on how to verify information and how to choose which information to pass along and which to allow to fade away before it is shared to another group via computer-mediated communication.

Because of the propensity of individuals for filling an information vacuum with whatever data they can find, health information professionals are bound to communicate the correct information as quickly as possible – and encourage selected Twitter opinion leaders to retweet

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this information. Doing so will stave off the negative effects of misinformation. As one could see from the content analysis, several posts were retweeted many times, extending the reach of certain users and working to create a series of opinion leaders in the new medium.

Organizational change agents such as those for the CDC and WHO should work to cultivate relationships with individual users who can be identified as leaders for large numbers of people – those in their particular social networks – in order to encourage better health behaviors. But first, they must ensure that the correct information is shared through computer-meditated communication.

Working with Twitter Opinion Leaders Research question 1a focuses on how opinion leaders and interpersonal communication networks are constituted on the channel known as Twitter. Through the use of the online survey, we were able to see how long individuals had been using Twitter, how many followers they had, and how many times they shared information throughout a given day. Rogers (2003) determined that a communication network brings together those who are in similar circles because of their socioeconomic status, level of education, or other similarities. Because users identify themselves on Twitter through several means – constructing their online personas through their profiles and pictures and the type of information posted for instance – one is able to determine easily which users to follow. When one has something in common with another individual on Twitter, he or she is more likely to follow the other user and place importance on the Tweets posted. Those individuals who have a greater number of followers can be considered users with greater influence; the ripples of their communication carry out further in the interpersonal communication channel. These users should be dubbed as opinion leaders because others actively seek out their opinions by following them.

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their communities because of their contact with many different areas, their cosmopolitan nature, and their willingness to examine new information (Rogers, 2003). Their ability to integrate new data into their established routines and belief systems leads one to understand that these individuals are clearly the type of people who should be implored to share information on health crises like the H1N1 outbreak with their less-worldly peers. Those charged with sharing important information designed to change health behaviors with others should examine the methods with which they should encourage Twitter opinion leaders – ones with large numbers of followers and a great deal of external trust in their statements – to share pertinent data designed to persuade others to adopt good health behaviors. When the power of these opinion leaders is harnessed, organizations will achieve higher adoption rates for all kinds of health behaviors, from stopping smoking to getting vaccinations to stop the spread of disease.

In order for organizations to use Twitter effectively to persuade individuals to adopt desirable health behaviors, they must work well with the opinion leaders established in the communication medium. First, organizations should identify opinion leaders in particular areas where they are trying to impact health behaviors in a positive way. Next, organizations should interact in a two-way conversation on Twitter with those who follow them – particularly opinion leaders. Finally, organizations should engage opinion leaders by giving them special access to correct information that these leaders can then share with their followers.

In general, individuals and organizations used Twitter to directly share information about the H1N1 health crisis. Their approaches were very similar to what one would expect with the diffusion of any innovation. While individuals communicated information in a manner that could be construed as interpersonal communication – where two-way communication was expected

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and welcomed – organizations used the tool more as a way to push information out to the masses. Neither approach to managing the communication stream is incorrect; they are simply different ways of sharing information. However, to utilize opinion leaders on Twitter in the best way possible, organizations should engage in authentic conversation with those leaders.

Organizations that wish to harness the power of conversation through opinion leaders should first identify these individuals by determining which individuals communicate regularly on pertinent topics. For instance, when a Twitter user talks extensively about wines, an organization can be sure that others who are interested in that topic will listen to his or her opinion about wine. By identifying individuals who are interested in similar topics, an organization has made the first step to working with opinion leaders to persuade individuals to adopt innovations.

If change agents work directly with these Twitter opinion leaders to communicate correct health information instead of allowing them to spread misinformation, then they will be sending message designed to reduce uncertainty for the population engaged on Twitter. As a result of helping these individuals connect with accurate health information, sometimes providing special access to information, organizations will gain the advantage of creating a new level of opinion leaders who are able to share pertinent data with their less cosmopolitan, less connected social networks.

In effect, every Twitter user could be used as an opinion leader, no matter whether he or she is an early adopter of health innovations or a later one. Each of these users is more connected with the outside world than counterparts who do not engage with the medium, and their behaviors can influence other sections of the population who are in need of good, solid health information. The content analysis clearly showed that individuals who needed health information

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on the H1N1 virus in the midst of an international call for vaccinations were able to find that data on Twitter, and they were able to make informed decisions about their health because of that information. No matter their level of connection with the medium or their number of followers, each one of these users serves as an opinion leader for others. Organizations should clearly be using their power to share pertinent information with the masses – through Twitter and other interpersonal channels.

Through the content analysis, I also learned the medium of Twitter can be used to share mundane information and important data; the key is to be vigilant as users and information consumers about how we use what is communicated. In addition, users whose primary goal is to share important information must understand the gravity of their position as opinion leaders within the Twitter community. In the same way that users verify information passed along in interpersonal communication, we must learn to authenticate data passed along through computermediated communication. For interpersonal communicators, that could mean performing research in a library or on the computer, and CMC offers users the same opportunities. The more we know about a user, the more we trust the information given. Therefore, we must be sure to learn about those we follow on Twitter through their profiles and by validating the information they share through whatever means we have available.

Implementation The final questions on the survey asked if individuals chose to adopt and implement the innovation in question – H1N1 vaccinations. This section – as well as the follow-up interviews with several users – followed users through the implementation stage in the innovation-decision process. In this phase, an individual actually utilizes a new idea (Rogers, 2003). Only about a fourth of the respondents to a question concerning whether or not they had vaccinated said that

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they had implemented the innovation. The CDC reported in January 2010 that about 20 percent of the total U.S. population was vaccinated (2010b). In addition, just over a quarter of the initial targeted groups for vaccination took advantage of the opportunity to receive a shot (2010b).

Respondents to the survey said that they had seen information on Twitter that related to H1N1 vaccinations, but this information did not give them all the information they needed to make such an important health decision. Many reported that they chose not to get the vaccine because of what other people said or that they chose preventative measures because shots were readily available. Whatever the reason for their decision, respondents agreed that Twitter was not their sole source for information regarding H1N1 vaccinations.

In the follow-up questions to the online survey, 10 users were asked again if they or their families received swine flu vaccinations and what information on Twitter helped them make the decision to adopt the innovation. In addition, they were asked which other information contributed to their decision to vaccinate or not. These individuals contributed to the earlier findings, stating that their decision was made with the help of family or because of vaccine availability. One user even stated that she did not make the decision “because of anything [she] saw on Twitter.” Users often found introductory material – filling the information stage of the innovation-decision process – from Twitter, but they made their final decision and completed the implementation phase with help from family, friends, or medical professionals.

In order to make the best use of Twitter as a vehicle for information that will bring individuals to adopt positive health behaviors, an organization or individual should ensure that the pertinent messages are not seen only on Twitter. For example, health messaging should be shared in print materials and advertisements – as well as through word of mouth – in addition to having information posted on Twitter and other computer-mediated communication channels.

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When multiple media are used, the best chance for adoption is achieved because prospective users are well informed about the benefits of adopting health behaviors. This research has led me to the conclusion that Twitter can help during the information stage of an innovation-decision process, but it cannot be used as the sole communication channel when trying to convince an individual to adopt an innovation. In the next section of this paper, I will discuss future research possibilities as well as recommendations for practical application of this research.

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Twitter and H1N1 Future Research The field of study focused on computer-mediated communication is so new that there is

very little research anchored in a empirical data. Therefore, the area is ripe for many new projects centered on CMC and how this field will continue to grow in the future. In this section of the project, I will discuss areas that should be explored in greater detail in order to help future scholars in their attempts to understand interpersonal communication, mass media, diffusion of innovations and the use of social media and CMC to accomplish an organization or individual’s goals to increase adoption of an idea or innovation. Among the opportunities for future research are examination of how the confirmation stage of the innovation-decision process is carried through CMC, analysis into Twitter communication about a health crisis very near to the actual crisis’ occurrence, and research into why individuals do not verify most of the information they receive on Twitter.

Though this project followed Twitter use and H1N1 through the first four steps of the innovation-decision process, one step remains to be examined. The confirmation phase, where individuals have their decision to adopt or reject an innovation reinforced, should be explored in depth (Rogers, 2003). In order to examine this phase and if Twitter influenced each user’s decision to vaccinate or not, researchers should conduct interviews or surveys with current users.

Questions could focus on whether or not they viewed information on H1N1 and vaccinations both before and after their decision was made and if that data made them feel more comfortable in their decision.

In addition to focusing on the steps involved in the innovation-decision process, future research should examine a new area of focus in diffusion study – Professor Arvind Singhal’s study of positive deviance and how this phenomenon can be used to impact specific behaviors. In

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particular, we should examine if Twitter can put individuals in the mind to act, or if there need to be more examples, demonstrations, and other ways to show that a behavior is desirable and beneficial. Even if individuals – as this research project shows – are not completely persuaded by the information they see on Twitter, they clearly perceive that the information passed along there is worthy of belief. What can make the difference and inspire them to act?

Since each of the respondents completed the survey during the months of March and April 2010 – almost a year after the H1N1 outbreak occurred – one must wonder if the survey was conducted too long after the height of the outbreak to garner an accurate representation of when individual users would have heard information about the pandemic. During the next health crisis, researchers should conduct surveys and interviews more quickly. For instance, when Seidel and Rogers conducted research into news diffusion following the September 11, 2001 terrorist attacks, they interviewed respondents in the first few days following the tragedy. The news of the event was fresh in individuals’ minds, and they were able to answer more accurately.

By taking advantage of the salience of the event, these Seidel and Rogers were able to get a more accurate picture of the spread of information and how it impacted behaviors.

In addition to moving more quickly to bring analysis into the diffusion of information through Twitter, future researchers should examine why individuals report that they do not verify the information they find on the social networking site. For instance, are the opinion leaders on Twitter so believable and charismatic that their communication is beyond reproach? Individuals may have changed their beliefs about opinion leaders in light of CMC, or they may have changed their perception of the information that is shared so easily and quickly via computers.

Researchers into diffusion theory could learn much about the every-changing quality of opinion leaders if they looked into why individuals trust Twitter communicators so quickly.

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Appendix A Interview Guide Opening of Interview Good afternoon, __________. Thank you for taking the time to talk to me about Twitter and information you may have learned about the H1N1 virus. News about the virus has dominated the media since last spring, and Twitter has been no exception. I have completed a content analysis of the information contained in six months worth of tweets about H1N1 and examined that information’s veracity as compared to information released by the CDC and Arkansas Department of Health.

First, we have to attend to the business of conducting this interview according to the guidelines established by UALR’s Institutional Review Board and to ensure confidentiality for all sources of information. So, I will outline our standard release form and information here. This interview is being conducted by Tonya Oaks Smith and Dr. Avinash Thombre of the Department of Speech Communication at UALR in order to understand the impact of Twitter on the spread of information related to the H1N1 virus. This information will aid us in determining the communication impact of this important new medium. Your responses to this interview are confidential and only the researcher/student has access to the interviews. Please review and sign the form below.

During our discussion, I will take notes and record your responses in my notebook. Once again, I assure you of the complete confidentiality of this interview. After the interview, I will take my notes and compare them with earlier analyses as well as with other interviews. These research methods will help me arrive at a clearer picture of what information is shared on Twitter

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and how people act on that information. Before we begin, do you have any questions for me about how the interview will proceed?

Questions Do you have a Twitter account? If so, how long have you used it to communicate with others?

How many tweets do you send in a typical day? Describe the process you go through in deciding what information to pass along to other users.

How many times do you retweet information? Describe which topics you choose to retweet.

How many followers do you have?

When did you first notice information about H1N1 on Twitter? Describe any information you remember seeing on Twitter regarding the H1N1 virus?

How did you verify the truth of that information?

Did you have an H1N1 shot this year?

How did reading information on Twitter contribute to your decision to get this vaccination?

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