Journal of Health Communication International Perspectives
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Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu Sarah C. Vos & Marjorie M. Buckner To cite this article: Sarah C. Vos & Marjorie M. Buckner (2016) Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu, Journal of Health Communication, 21:3, 301-308, DOI: 10.1080/10810730.2015.1064495 To link to this article: http://dx.doi.org/10.1080/10810730.2015.1064495
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Date: 26 February 2016, At: 10:24
Journal of Health Communication, 21:301–308, 2016 Copyright # Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2015.1064495
Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu SARAH C. VOS1 and MARJORIE M. BUCKNER2 1
Department of Communication, University of Kentucky, Lexington, Kentucky, USA Department of Communication Studies, Texas Tech University, Lubbock, Texas, USA
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2
Limited research has examined the messages produced about health-related crises on social media platforms and whether these messages contain content that would allow individuals to make sense of a crisis and respond effectively. This study uses the crisis and emergency risk communication (CERC) framework to evaluate the content of messages sent via Twitter during an emerging crisis. Using manual and computer-driven content analysis methods, the study analyzed 25,598 tweets about the H7N9 virus that were produced in April 2013. The study found that a large proportion of messages contained sensemaking information. However, few tweets contained efficacy information that would help individuals respond to the crisis appropriately. Implications and recommendations for practice and future study are discussed.
When a health crisis emerges, risk and crisis communication research emphasizes the need to communicate information that encourages individuals to (a) make sense of what is happening and (b) respond effectively (Reynolds, 2006; Reynolds & Seeger, 2005). The existing models of how this communication should occur assume that officials will disseminate messages through traditional media outlets, like newspapers and television stations that distribute information using a one-to-the-many model. However, unlike the one-tothe-many format of traditional media, social media platforms encourage users to spread information through their networks. This network format limits officials’ ability to assess whether the information that is disseminated would allow individuals to make sense of an event and respond effectively. This study seeks to address this concern by analyzing the types of information distributed via social media during a crisis. Social media use increases during crisis events as people seek information about the event itself and check on family and friends (Faustino, Liu, & Jin, 2012). Given the importance of sensemaking and efficacy information during a crisis, practitioners and researchers need to assess whether the content of social media messages can contribute to an effective response. As a result, understanding the types of information that people send via social media during risk and crisis events is of practical interest. To this end, we use the crisis and emergency risk communication (CERC) framework (Reynolds & Seeger, 2005; Veil, Reynolds, Sellnow, & Seeger, 2008) as a theoretical model to analyze
Address correspondence to Sarah C. Vos, Department of Communication, University of Kentucky, 230 Grehan Journalism Building, Lexington, KY 40506, USA. E-mail: sarah.vos@ uky.edu
whether messages sent via one social media platform, Twitter, during a recent public health event, the 2013 H7N9 outbreak, provide information that would allow individuals to make sense of and respond effectively to this crisis.
CERC: Precrisis Sensemaking and Efficacy Messages The CERC framework addresses the ongoing and escalating communication processes that occur at different stages of risk and crisis events (Veil et al., 2008). The framework draws on existing theories of risk, crisis, and health communication to identify best communication practices during a crisis. Some of the key theoretical concepts that inform CERC include sensemaking (Weick, 1988) and efficacy (Witte, 1992). The CERC framework divides crises into five stages (i.e., precrisis, initial event, maintenance, resolution, and evaluation) and identifies appropriate message strategies for each stage. During the emerging stages (i.e., precrisis and initial event), the framework calls for sensemaking messages to educate the public about the nature of the risk and efficacy messages to encourage appropriate responses (Reynolds & Seeger, 2005). According to CERC, the information conveyed in sensemaking and efficacy messages affects the way a crisis develops by influencing how individuals respond (Veil et al., 2008). In other words, if people recognize an emerging crisis and respond appropriately, the crisis may be contained or, at least, reduced in scope. Sensemaking messages contribute to effective response by allowing individuals to make sense of the uncertainty inherent in a health-related crisis (Veil et al., 2008). The concept of sensemaking in CERC is grounded in Weick and colleagues’ work on sensemaking (Weick, 1988, 1995; Weick, Sutcliffe, & Obstfeld, 2005). According to this conception, sensemaking is a process through which individuals
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302 search for meaning in response to uncertainty and ambiguity (Weick et al., 2005). The sensemaking process is not necessarily driven by facts or truth. Instead, individuals seek plausibility as they incorporate more information into their understanding of an event. The process ‘‘simultaneously generates the raw material that is used for sensemaking and affects the unfolding of the crisis itself’’ (Weick, 1988, p. 305). Sensemaking contributes to the creation of meaning and influences future events as individuals act on that meaning. Weick (1995) identified five ways in which people engage in sensemaking: (a) placing events into frameworks, (b) comprehending what has happened, (c) accommodating the unexpected, (d) interacting to produce understanding, and (e) identifying patterns. During crises, sensemaking allows people to understand what is going on and evaluate potential responses (Reynolds & Seeger, 2005; Veil et al., 2008). Messages that encourage sensemaking provide information that frames the scope of the crisis. These messages include information about the probability of something happening in the future and messages about the current developments surrounding a crisis. Efficacy messages contribute to effective response by instructing people how to respond appropriately. These messages promote beliefs that the recommended response will work (response efficacy) and that individuals have the ability to perform the recommended response (self-efficacy; Coombs, 2009). Messages that encourage self-efficacy and response efficacy can influence how people respond to threatening situations (Coombs, 2009; Egbert & Parrott, 2001). As a result, efficacy messages can reduce the risk of a crisis as individuals prepare and learn how to respond appropriately (Veil et al., 2008). The content that creates efficacy depends on the type of crisis. For example, during a potential pandemic flu, efficacy messages should address nonvaccine infection-control measures (Reynolds & Quinn, 2008). The Centers for Disease Control and Prevention (CDC) advocates several strategies to limit the spread of viruses without a vaccine, including (a) basic infection control with frequent hand washing and cough covering, (b) voluntary home quarantine of ill and non-ill family members for at least 4 days, (c) the dismissal of students from school and the closing of child care programs, and (d) social distancing through the canceling of large events. Sensemaking and Efficacy on Social Media Despite the importance of communicating sensemaking and efficacy messages during an emerging crisis such as a potential pandemic, little research has examined the presence of such messages in social media. Social media include a variety of Internet platforms that allow individuals to communicate by producing and consuming content. Although the platforms vary in the affordances they provide to users, most platforms allow individuals to communicate with other users within a user-defined network. Twitter, for example, allows users to send messages (tweets) of up to 140 characters to other users (organizations, governments, or individuals), hashtags (short words or phrases beginning with a pound
S. C. Vos and M. M. Buckner sign that function like discussion boards), and followers (other Twitter users who receive message updates directly from another user’s account). The Twitter interface encourages users to follow people with whom they may or may not have an interpersonal relationship. These relationships are not necessarily reciprocal: One Twitter user may follow a second Twitter user, but the second Twitter user may not follow the first Twitter user. Users broadcast information to their followers, and the service encourages users to retweet or to pass on messages they receive to their followers. Since the service first went live in 2006, Twitter has grown to 284 million active users worldwide (Twitter, 2015). Previous examinations of social media use during health crises have tended to focus on disease tracking (Chew & Eysenbach, 2010; Signorini, Segre, & Polgreen, 2011). For example, during the 2009 H1N1 flu pandemic, Chew and Eysenbach (2010) conducted a content analysis of 5,395 tweets related to the pandemic. The purpose of the study was to demonstrate that Twitter could be used to track an epidemic, specifically attitudes and behaviors about the epidemic. The researchers found that more than half of the tweets communicated news and information about the virus, and 22.5% of tweets communicated a personal experience. The vast majority of tweets that provided information contained a link to a reference that individuals could follow to validate the information in the tweet. However, those references rarely led to traditional public health and government authorities like the World Health Organization (WHO) or the CDC. Signorini and colleagues (2011) also examined chatter about the 2009 H1N1 outbreak on Twitter. Their analysis focused on public sentiment in response to the outbreak and disease activity. Neither study assessed whether the messages contained content that could help the public (a) engage in sensemaking or (b) respond appropriately to the crisis. Since the H1N1 outbreak in 2009, Twitter use has increased rapidly in the United States (Duggan & Brenner, 2013) and across the globe (McCue, 2013). Because of the increased popularity of social media and, in particular, Twitter, official organizations are using the medium as a way to communicate with a large, general audience. Both the WHO and the CDC maintain Twitter accounts and use these accounts to tweet about potential public health crises. In April 2013, the WHO tweeted regularly to its 789,000 followers about H7N9, sending out efficacy messages and updates on the number of cases and deaths (WHO, Twitter page, April 26, 2013). The CDC maintains several Twitter accounts, including one dedicated to flu information, @CDCFlu. In April 2013, the CDC flu account provided information about H7N9 to its 246,000 followers. By communicating via Twitter, the WHO and CDC can quickly spread information to a large number of followers. Research suggests a positive relationship between the number of followers and the reach of a Twitter account’s messages (Suh, Hong, Pirolli, & Chi, 2010). However, the impact of these organizations on the public conversation about health-related issues on social media platforms has not been examined.
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Tweeting Bird Flu Based on this review, this study seeks to answer the following research questions: Research Question 1: During an emerging public health crisis, do messages delivered via social media contain content that would allow individuals to make sense of what is happening?
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Research Question 2: During an emerging public health crisis, do messages delivered via social media contain content that would allow individuals to develop efficacy, should the event transform into a crisis?
Methods The 2013 outbreak of the H7N9 virus, a variant of the bird flu, was chosen to examine social media messages in the emerging stages of a risk and crisis event. Although not a full-blown crisis, the outbreak had the potential to evolve into a pandemic, as defined by the CDC (Reynolds & Quinn, 2008): (a) The H7N9 virus was novel, meaning that humans had not been exposed to it before and therefore would have little immunity to it; (b) the virus was deadly; and (c) at the time of data collection, it appeared to be spreading (Bradsher, 2013b). The Chinese government announced the discovery of the virus on March 31 (Bradsher, 2013b). Within 10 days, the virus had infected 24 people and killed seven (Bradsher, 2013a). On Twitter, chatter about the H7N9 virus quickly reached thousands of ‘‘relevant’’1 tweets per day (Topsy Pro Analytics, 2013). In this study, we analyzed 25,598 unique tweets about the virus produced between April 10 and April 18. Data Collection Starting April 17, 2013, tweets were gathered using Twitter’s application programmer’s interface. At the time, the service gave access to a subset of the tweets produced at any given time and allowed tweets to be gathered up to 7 days in the past. A total of 25,598 unique tweets were collected using four sets of search terms: H7N9, avian and flu, bird and flu, and China and flu. The 25,598 tweets were produced by 14,430 unique Twitter accounts. Individual accounts contributed between 1 and 163 tweets in the data set, with most accounts contributing one tweet (Mo ¼ 1, Mdn ¼ 1, M ¼ 1.77) to the data set. Four accounts contributed more than 90 tweets each to the data set: @AngryPigss (163 tweets; unidentified account), @Crof (123 tweets; account identified as a retired Canadian college teacher and writer), @ah1n1news (104 tweets; account identified as Andrew, a self-proclaimed H1N1 journalist from Arizona), and @ironorehopper (93 tweets; account identified as a multilingual blogger). 1
According to Topsy Pro, relevant tweets are those that include a link or have been retweeted or passed on to another user. Their models do not include tweets that (a) are not retweeted or (b) do not contain a link.
Most of the sample consisted of original tweets. Approximately one third of the tweets were identified as retweets (34.1%, n ¼ 8,720). Approximately three fourths contained hyperlinks (78.3%, n ¼ 20,045). Analysis of the Tweets The coding scheme was developed using the CERC framework (Reynolds & Quinn, 2008; Reynolds & Seeger, 2005) and a pilot coding of 3,000 tweets that were randomly selected from the total. The scheme assessed the type of information communicated. In order to be included in a category, the information had to be present in the text of the tweet, or the tweet had to clearly indicate that the information was available in the link provided. It was beyond the scope of this study to examine the content of links. Intercoder Reliability Intercoder reliability was assessed using a two-stage process. In the first stage, the reliability of the coding scheme itself was established using a manual coding process (Krippendorff, 2004). After a brief training session, we manually coded 200 tweets. Krippendorff’s alpha was used to measure intercoder reliability, using the SPSS macro created by Hayes and Krippendorff (2007). Coder agreement exhibited strong reliability (a > .7; Krippendorff, 2004) or a high percent agreement (>90%). Once reliability was established for the manual coding, an automated coding scheme was developed using KH Coder (Higuchi, 2013), a software that provides the capability to develop complex codes based on individual words, word combinations, words in proximity, and and-not combinations (e.g., ‘‘viral’’ and ‘‘not microblog’’). The second stage of reliability assessment established that the automated coding scheme reliably coded tweets compared with the manual coding. Using an iterative process, we assessed the automated coding by examining tweets in each coding category, tweets that did not code in any category, tweets that coded in more than one category, and random selections of 1% of the data set. These checks were used to refine the automated coding until it coded with a high degree of reliability compared to manual coding, as assessed by Krippendorff’s alpha (a > .899; Krippendorff, 2004). Measures Each tweet was coded for two types of information: sensemaking and efficacy. The categories were distinctive but not exclusive. Tweets were coded for each category separately; individual tweets could be coded for more than one category. Sensemaking. Tweets that contained content that could allow individuals to make sense of the emerging crisis were categorized as sensemaking tweets. Following Weick’s (1995) description of the sensemaking processes, these tweets contained information that showed individuals placing events into frameworks, comprehending what was happening, accommodating the unexpected, interacting to produce understanding, and identifying the pattern of events. Sensemaking tweets contained information about the number of people infected, the number of deaths, the spread of
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304 the virus, vaccine development, and the likelihood of human-to-human transmission. Tweets also included nonfactual information and evaluations of the severity of the crisis. Intercoder reliability for sensemaking was high (manual coding, Krippendorff’s a ¼ .078; computer coding, Krippendorff’s a ¼ .09; Krippendorff, 2004). Efficacy. The category of efficacy captured tweets that provided information that could encourage self-efficacy and response efficacy (Reynolds, 2006; Reynolds & Quinn, 2008). Information provided in these tweets included basic disease control instruction (e.g., frequent hand washing, cough covering), cooking instructions (e.g., preparing chicken), and travel advice or restrictions. The two coders established a high percent agreement (90.5%); however, intercoder reliability for the manual coding of efficacy was low (a ¼ .3; Krippendorff, 2004). This may have been because of the small number of tweets (6=200) in the sample that contained efficacy information. The two coders resolved disagreements through discussion. Computer coding exhibited a high level of agreement with the manual coding (a ¼ .1; Krippendorff, 2004). Following the establishment of reliability, a computer analysis of all 25,598 tweets was conducted. The coded tweets were then pulled into SPSS for analysis.
Results A large proportion of the analyzed tweets contained sensemaking information about the virus (n ¼ 22,612, 88.3%). Sensemaking tweets placed the crisis in a framework, provided details of cases and deaths, demonstrated how individuals were accommodating the unexpected, identified patterns in the crisis, and suggested interaction. Most of the sensemaking tweets contained hyperlinks (n ¼ 18,081, 80%). Tweets placed the emerging crisis in a framework by associating the virus with other negative events. One tweet exclaimed, ‘‘Bird flu will wipe out China, obesity will wipe out USA and mad cow disease will take kut UK. Korea will just get nuked.’’2 Other tweets placed the event in a framework by dismissing it as a media frenzy: ‘‘Bird flu pandemic from China and nukes in North Korea; nothing like some fear mongering from the media.’’ (See Table 1 for additional examples of each tweet type.) Other tweets allowed people to comprehend what was happening by reporting details of the crisis. Tweets provided updates about the number of cases of H7N9: ‘‘Record Number of 14 H7N9 Cases Reported Today 15 H7N9 cases have been reported . . . ’’ Tweets also provided information about the number of deaths: ‘‘Bird flu death toll in China rises to 14.. . .’’ In addition, tweets included information about how the virus was spread: ‘‘RT @HaertlG: We are exploring the possibility that #H7N9 can be spread between people; as yet there is no evidence of sustained H2H transmission.’’ Sensemaking tweets showed individuals accommodating the unexpected through exclamations and reactions. ‘‘Oh my god!!’’ tweeted one user, ‘‘Bird flu started again n china. 2
Tweets are reproduced verbatim. As a result, they may contain typos.
S. C. Vos and M. M. Buckner Can’t travel any country these days. Everywhere there’s some issues: (.Better stay home:P.’’ In another tweet, a user remarked, ‘‘You telling me I’m going to have to deal with the West Nile & the muthafuckin Bird Flu this summer? crotch grab #ItsHardOutHereForAPimp.’’ In addition, tweets showed individuals identifying patterns in the emerging crisis. One user tweeted, ‘‘Pattermomg @live_h1n1 This isn’t really a surprise. Wrote about this a few years back on #H5N1 http://t.co/7nV4bhyVc2 Still relevant to #H7N9.’’ Another user tweeted, ‘‘The world is becoming more and more wild with daily serious events incidents taking place such as H7N9 Boston bombings Inmates escape.’’ Tweets also contained content that suggested interaction. One user tweeted, ‘‘Keep an eye on this one: Report on 3 in China Who Died From Bird Flu Points to Severity of Strain http://t.co/jb4NXaEYAX.’’ Another user tweeted, ‘‘A few comments about #H7N9 in #China 1) We still don’t know much. 2) To date 33 known cases & 9 deaths http: //t.co/aYslVd8MgU–>#fb.’’ Less than 2% (n ¼ 423) of the sample tweets contained efficacy information. The majority of these tweets included hyperlinks (n ¼ 254, 60%). These tweets included information about the lack of a travel restriction to China. For example: ‘‘RT @CDCtravel: #H7N9 fact: there’s currently NO recommendation against travel to China.. . .’’ They provided advice about airport screening: ‘‘I follow @19AlexG ! Flu screening at airports best done prior to travel: Screening people leaving a country . . . ’’ Efficacy tweets emphasized the importance of eating well-cooked meat: ‘‘RT @WHO: You wont become infected with #flu virus through eating well-cooked meat & food incl. chicken, other poultry products #foodsafety #H7N9.’’ Other tweets in this category encouraged travelers to avoid animals: ‘‘Dr. Obvious from CDC gives advice to travelers: Do not touch pigs in China http://t.co/34y2p2MIvd.’’ It is noteworthy that some of the advice provided in efficacy tweets was not necessarily supported by scientific evidence. One user tweeted, ‘‘How to avoid getting H7N9 virus: Be a vegetarian from now on.’’ A small number of tweets contained both sensemaking information and efficacy information (n ¼ 154, 0.6%). For example, one user tweeted, ‘‘Hoping everyone can wash their hands carefully and has less contacting with chickens and birds, the H7N9 flu is crazy in China!’’ Another tweeted, ‘‘Wash your hands! Deaths from new bird flu underscore grim fears, reports show http://t.co/aBJEIjoCBP via @NBCNewsHealth.’’ Approximately 11% (n ¼ 2,717) of the tweets did not reflect sensemaking or efficacy information. These tweets did not contain enough information to assess how they contributed to sensemaking or efficacy. Most of these tweets contained hyperlinks (n ¼ 1,842, 67.8%). For example, tweets that mentioned the virus but did not add additional information were not coded for either category: ‘‘Epidemiological update of 15 April: avian influenza A(H7N9) virus in China . . . http://t.co/KUijabAycY.’’ Other tweets in this category included clearly spurious claims of having the bird flu: ‘‘@davefulll tellmrs gallianagh I have bird flu and cant do my orals.’’ These tweets also included references to the bird flu that were meant as jokes but did not contain
Tweeting Bird Flu
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Table 1. Examples of tweets Sensemaking (n = 22,612, or 88.3%) Placing the crisis in a framework . I feel like health epidemics like bird flu are just ways to stimulate an Economy . @ChaoxLoL Hey, Chaox. I’m in Shanghai right now and I just wanted to let you know that the Bird Flu is not really a big deal. Media overhype . And this new H7N9 is believed to now jump from human to human. Fuck This is contagion Comprehending what is happening .:O @STcom: #China reports new #H7N9 bird flu death, 2 new infections http://t.co/HXMADDxMwY . RT @CDCFlu Ongoing community spread is needed for a pandemic to start that has not been seen in China with #H7N9 . According to @WHO, so far there are 38 confirmed cases infected with new strain H7N9 in China, 10 deaths and 19 severe. eldely predominant. Accommodating the unexpected . bird flu is back wtf . RT @nigelcameron: Please people, be scared. It’s comimg. RT @joergheber: 5 new confirmed RT @WHO: Following #H7N9? http://t.co/PcsU5MTMI7#flu#risk . RT @princebhojwani: Woah, that was fast. RT @BloombergNews: China confirms 77 H7N9 infections, 16 deaths: Xinhua Interacting . Worth reading. Latest on bird flu in china. http://t.co/1twppwC8JO . @setiogi Thanks for the #H7N9 updates! My grandpa had the 1918 #SpanishFlu as a baby this is a story I care about. Glad to be informed! . RT @WHO: #H7N9 in China: We will be watching for signs of human-to-human transmission and new cases in other geographical areas. #influenza Identifying patterns: . The world is becoming more and more wild with daily serious events incidents taking place such as H7N9 Boston bombings Inmates escape . Whenever I start my deadly pandemics in Plague Inc., I always start it in China. #H7N9 . #birdflu hits the UK. Will this turn out to be #h7n9? Hopefully not. http://t.co/QxYvsyFwtw Efficacy (n = 423, or 1.7%) . CDC offers H7N9 avian flu advice for travellers and expats in China: http://t.co/xaLDtEBtdT . RT @WHO: Prevent #H7N9: Wash hands, utensils & chopping board after handling raw meat, newly slaughtered animals, esp.poultry #foodsafety . @adamlambert be extra cautious in china. Bring along hand sanitizer and dun eat unhygienic food! Be careful of bird flu! And have fun! Sensemaking and Efficacy (n = 154, or 0.6%) . @hcfischer1 Would you rather die from bird flu or a nuclear strike? Caution around birds and eating eggs=poultry always a good idea in SEA. . Best #F1Shanghai advice from @raerity: Not the trip 2 be adventurous w= food: 24 avian flu deaths, 16 K pigs in the river. Eat clean & wise. . Bird is on the way . . . so Make sure all of you people Spread the Word! THE BIRD FLU IS COMING! So make sure too wash your hands 7xs a Day Other (n = 2,717, or 10.6%) . @BarryTuck take ACC200 effervescent tablets. Makes chicken soup look like a some weird lovechild of Avian and Swine flu. . ‘‘I go in there, I get bird flu, I don’t come out . . . ’’ #30rock . Your tweets are giving people the bird flu, please STFU
information that would allow for sensemaking or efficacy about the virus itself. One joke was repeated by several different users: ‘‘Whats the difference between bird flu and swine flu?. For bird flu you need TWEETMENT, for swine flu you need OINK-MENT. Ill shutup na.’’
Discussion Social Media as a Medium for Sensemaking The results of this study suggest that social media provides a forum where individuals can make sense of an emerging
crisis, fulfilling an important need as outlined by CERC (Veil et al., 2008). Over the 9-day study period, messages distributed via Twitter contained content that would allow for sensemaking using the processes outlined by Weick (1995). Tweets provided information that placed the emerging crisis into a framework. Tweets contained information that would allow individuals to comprehend what was happening by reporting on the number cases and the spread of the virus. Tweets showed individuals accommodating the unexpected and interacting to produce understanding, and tweets identified patterns.
306 Taken together, these messages had the potential to help individuals to make sense of the emerging crisis. From the messages, individuals could have concluded that a serious virus with pandemic potential had emerged in China. However, the messages also communicated that the virus was not spreading easily from individual to individual and that officials were monitoring the situation. Although some of the tweets contained misinformation, the information communicated in the sensemaking tweets mirrored much of the information contained in the official report written by the WHO (2013a) after the outbreak ended.
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Designing Efficacy Messages for Social Media The results suggest that Twitter did not provide a forum for efficacy information. The low proportion of tweets communicating information that could enhance efficacy is troubling, as research suggests that efficacy information is critical to encouraging appropriate response (Reynolds & Seeger, 2005). The small proportion of efficacy tweets may be the result of the emerging nature of the crisis and the geographical distance of the event for many English-language Twitter users. At the time these tweets were collected, the virus had not spread outside of China, and it never did (WHO, 2013b). This study was not able to assess tweets in languages other than English, and it is possible that efficacy information was disseminated in other languages via Twitter or on another social media platform (e.g., the Chinese platform Sino Weibo). However, the WHO and CDC, two major organizations in global public health, tweeted about the crisis in English and provided efficacy messages in English. In addition, CERC recommends that efficacy messages encourage appropriate response and a belief that the response will work (Veil et al., 2008). During the time period of this study, four to six new patients were identified every day, and people were dying (WHO, 2013b). In addition, no one knew how the virus was spreading (CDC, 2013a). Although this event never transitioned into a pandemic, the event had pandemic potential (CDC, 2013b). The study did find some messages that discussed washing hands and eating only fully cooked poultry. Yet many of the behaviors recommended by the CDC (2013a) specific to this outbreak as well as behaviors recommended for a more general outbreak (Reynolds, 2006) did not appear in the data set. In addition, the messages did not include any information that would encourage response efficacy (Coombs, 2009), the belief that the recommended measures would work. These findings are particularly troubling as research suggests that individuals do not understand the role they play in preventing the spread of viruses with pandemic potential (Elledge, Brand, Regens, & Boatright, 2008). Elledge and colleagues (2008) found that individuals did not understand the effectiveness of nonvaccine control measures like voluntary quarantines that would be necessary if a virus reached the pandemic stage. Although tweeting all of the strategies the CDC has identified for infection control at an initial
S. C. Vos and M. M. Buckner crisis stage may not be necessary, the CERC framework suggests that certain messages would be appropriate (Reynolds, 2006). For example, messages could promote washing hands, covering coughs, and maintaining social distance (e.g., staying home from work, not traveling) when experiencing symptoms. Messages might also promote response efficacy and educate individuals that nonvaccine control measures work. In the case of an avian flu, messages should also promote not touching live or dead animals when traveling and eating only food that is fully cooked. Although several of these message types appeared in the data set, less than 2% of the tweets collected contained efficacy information. Thus, efficacy messages had a low presence in the public discourse about H7N9 on Twitter. There are several possible reasons why this occurred. One possibility is that the WHO and the CDC were not tweeting enough efficacy messages to have a substantial influence on the Twitter conversation about H7N9. At least one study has shown a lack of efficacy messages in the social media response of official organizations. Sutton, League, Sellnow, and Sellnow (2015) found that in a public health crisis that involved a flood and water contamination, a low proportion of messages sent by governmental organizations contained efficacy information. Another possibility is that there were few efficacy messages because of the networked nature of social media platforms. Even if the CDC and the WHO were tweeting efficacy messages, those messages would only have a large reach if their followers were retweeting those messages. For many English-language Twitter users, messages about a far-off, emerging crisis may not have been relevant enough to encourage the frequency of retweeting that would have resulted in a substantial proportion of efficacy messages. However, CERC suggests that efficacy information be distributed before a virus has reached a pandemic stage (Reynolds, 2006). As a practical matter, this means that officials need to distribute efficacy messages before they are relevant to individuals. By the time a pandemic virus has spread far enough for efficacy messages to be relevant to more users, the messages may be too late. This suggests that researchers and practitioners need to understand how to compose social media messages in a manner that would encourage redistribution through a networked platform, even if those messages lack strong relevance. In other words, researchers and practitioners need to design messages for social media to encourage distribution throughout the network. These messages should draw on existing research about social media messages and factors that increase the redistribution of messages. For example, a large proportion of the efficacy messages contained a hyperlink to an Internet website. Existing research suggests that the inclusion of a hyperlink reduces the reach of a message by discouraging (or at least not encouraging) the redistribution of a message through a network in other contexts (Malhotra, Malhotra, & See, 2012; Sutton et al., 2013). In addition, when message designers depend on the contents of a Web page to convey information (e.g., ‘‘CDC offers H7N9 avian flu advice for
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Tweeting Bird Flu travellers and expats in China: http://t.co/xaLDtEBtdT’’), they lose an opportunity to provide immediate, actionable information: Not everyone who sees the tweet will click on the hyperlink. Although these choices may seem small, they influence who is exposed to a message and how many people are exposed to the message. Research suggests that the more often people are exposed to a message encouraging protective action, the more likely they are to respond appropriately (Wood et al., 2012). More work needs to be done to understand what message elements contribute to redistribution on social media and how social media platforms can be used to encourage redistribution. These results suggest that in addition to distributing efficacy messages prior to a pandemic, practitioners need to distribute more efficacy messages via social media during future potential outbreaks. These messages should contain more, specific information about how to protect oneself against exposure, and the messages should encourage response efficacy, in particularly the belief that nonvaccine infection-control measures are effective (Reynolds & Quinn, 2008). Limitations and Future Research By only using tweets to assess information communicated about H7N9, this study does not account for information spread through other social or traditional media channels. This study also included a limited time frame. In addition, the purpose of this study was to assess the public discussion of H7N9, and thus the study does not provide an analysis of what any one individual Twitter user was exposed to. However, this study does assess how an emerging public health crisis is discussed on a social media platform. In light of the study’s findings, future research should explore the lack of self-efficacy messages present in social media discourse about health crises, consider the accuracy of information shared, and examine whether message design could encourage wider dissemination of efficacy messages on social media. Most notable is that scholars should examine whether the lack of efficacy messages is unique to a potential pandemic crisis or also exists in other social media discussions of health-related crises. Though beyond the scope of this study, examining the accuracy of sensemaking and efficacy tweets may inform scholars and practitioners about the quality of information disseminated during health crises. In addition, future research should examine the distribution of efficacy messages from organizations like the WHO and CDC in order to assess retweet frequency and how specific message elements relate to wider tweet dissemination. Such a study might allow officials to design messages that are more likely to be retweeted and thus more likely to be spread to large numbers of users.
Conclusion When a crisis emerges, a key way people obtain and send information is through social media (Faustino et al., 2012). At the same time, health-related crises create situations in which officials need to communicate effectively and rapidly
through many communication channels (Ulmer, Sellnow, & Seeger, 2011). Two key types of information that need to be communicated are sensemaking and efficacy messages (Veil et al., 2008). This study demonstrated that in the emerging phases of a health-related crisis, the type of content distributed through social media allowed for sensemaking. However, this study found that few tweets contained efficacy information, and as a result social media was not an effective platform for distributing information that could contribute to appropriate response.
Acknowledgments We thank Timothy Sellnow, PhD, and Zixiu Tai, PhD, at the University of Kentucky for their comments on earlier versions of this article.
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ISSN: 1081-0730 (Print) 1087-0415 (Online) Journal homepage: http://www.tandfonline.com/loi/uhcm20
Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu Sarah C. Vos & Marjorie M. Buckner To cite this article: Sarah C. Vos & Marjorie M. Buckner (2016) Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu, Journal of Health Communication, 21:3, 301-308, DOI: 10.1080/10810730.2015.1064495 To link to this article: http://dx.doi.org/10.1080/10810730.2015.1064495
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Journal of Health Communication, 21:301–308, 2016 Copyright # Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2015.1064495
Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu SARAH C. VOS1 and MARJORIE M. BUCKNER2 1
Department of Communication, University of Kentucky, Lexington, Kentucky, USA Department of Communication Studies, Texas Tech University, Lubbock, Texas, USA
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2
Limited research has examined the messages produced about health-related crises on social media platforms and whether these messages contain content that would allow individuals to make sense of a crisis and respond effectively. This study uses the crisis and emergency risk communication (CERC) framework to evaluate the content of messages sent via Twitter during an emerging crisis. Using manual and computer-driven content analysis methods, the study analyzed 25,598 tweets about the H7N9 virus that were produced in April 2013. The study found that a large proportion of messages contained sensemaking information. However, few tweets contained efficacy information that would help individuals respond to the crisis appropriately. Implications and recommendations for practice and future study are discussed.
When a health crisis emerges, risk and crisis communication research emphasizes the need to communicate information that encourages individuals to (a) make sense of what is happening and (b) respond effectively (Reynolds, 2006; Reynolds & Seeger, 2005). The existing models of how this communication should occur assume that officials will disseminate messages through traditional media outlets, like newspapers and television stations that distribute information using a one-to-the-many model. However, unlike the one-tothe-many format of traditional media, social media platforms encourage users to spread information through their networks. This network format limits officials’ ability to assess whether the information that is disseminated would allow individuals to make sense of an event and respond effectively. This study seeks to address this concern by analyzing the types of information distributed via social media during a crisis. Social media use increases during crisis events as people seek information about the event itself and check on family and friends (Faustino, Liu, & Jin, 2012). Given the importance of sensemaking and efficacy information during a crisis, practitioners and researchers need to assess whether the content of social media messages can contribute to an effective response. As a result, understanding the types of information that people send via social media during risk and crisis events is of practical interest. To this end, we use the crisis and emergency risk communication (CERC) framework (Reynolds & Seeger, 2005; Veil, Reynolds, Sellnow, & Seeger, 2008) as a theoretical model to analyze
Address correspondence to Sarah C. Vos, Department of Communication, University of Kentucky, 230 Grehan Journalism Building, Lexington, KY 40506, USA. E-mail: sarah.vos@ uky.edu
whether messages sent via one social media platform, Twitter, during a recent public health event, the 2013 H7N9 outbreak, provide information that would allow individuals to make sense of and respond effectively to this crisis.
CERC: Precrisis Sensemaking and Efficacy Messages The CERC framework addresses the ongoing and escalating communication processes that occur at different stages of risk and crisis events (Veil et al., 2008). The framework draws on existing theories of risk, crisis, and health communication to identify best communication practices during a crisis. Some of the key theoretical concepts that inform CERC include sensemaking (Weick, 1988) and efficacy (Witte, 1992). The CERC framework divides crises into five stages (i.e., precrisis, initial event, maintenance, resolution, and evaluation) and identifies appropriate message strategies for each stage. During the emerging stages (i.e., precrisis and initial event), the framework calls for sensemaking messages to educate the public about the nature of the risk and efficacy messages to encourage appropriate responses (Reynolds & Seeger, 2005). According to CERC, the information conveyed in sensemaking and efficacy messages affects the way a crisis develops by influencing how individuals respond (Veil et al., 2008). In other words, if people recognize an emerging crisis and respond appropriately, the crisis may be contained or, at least, reduced in scope. Sensemaking messages contribute to effective response by allowing individuals to make sense of the uncertainty inherent in a health-related crisis (Veil et al., 2008). The concept of sensemaking in CERC is grounded in Weick and colleagues’ work on sensemaking (Weick, 1988, 1995; Weick, Sutcliffe, & Obstfeld, 2005). According to this conception, sensemaking is a process through which individuals
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302 search for meaning in response to uncertainty and ambiguity (Weick et al., 2005). The sensemaking process is not necessarily driven by facts or truth. Instead, individuals seek plausibility as they incorporate more information into their understanding of an event. The process ‘‘simultaneously generates the raw material that is used for sensemaking and affects the unfolding of the crisis itself’’ (Weick, 1988, p. 305). Sensemaking contributes to the creation of meaning and influences future events as individuals act on that meaning. Weick (1995) identified five ways in which people engage in sensemaking: (a) placing events into frameworks, (b) comprehending what has happened, (c) accommodating the unexpected, (d) interacting to produce understanding, and (e) identifying patterns. During crises, sensemaking allows people to understand what is going on and evaluate potential responses (Reynolds & Seeger, 2005; Veil et al., 2008). Messages that encourage sensemaking provide information that frames the scope of the crisis. These messages include information about the probability of something happening in the future and messages about the current developments surrounding a crisis. Efficacy messages contribute to effective response by instructing people how to respond appropriately. These messages promote beliefs that the recommended response will work (response efficacy) and that individuals have the ability to perform the recommended response (self-efficacy; Coombs, 2009). Messages that encourage self-efficacy and response efficacy can influence how people respond to threatening situations (Coombs, 2009; Egbert & Parrott, 2001). As a result, efficacy messages can reduce the risk of a crisis as individuals prepare and learn how to respond appropriately (Veil et al., 2008). The content that creates efficacy depends on the type of crisis. For example, during a potential pandemic flu, efficacy messages should address nonvaccine infection-control measures (Reynolds & Quinn, 2008). The Centers for Disease Control and Prevention (CDC) advocates several strategies to limit the spread of viruses without a vaccine, including (a) basic infection control with frequent hand washing and cough covering, (b) voluntary home quarantine of ill and non-ill family members for at least 4 days, (c) the dismissal of students from school and the closing of child care programs, and (d) social distancing through the canceling of large events. Sensemaking and Efficacy on Social Media Despite the importance of communicating sensemaking and efficacy messages during an emerging crisis such as a potential pandemic, little research has examined the presence of such messages in social media. Social media include a variety of Internet platforms that allow individuals to communicate by producing and consuming content. Although the platforms vary in the affordances they provide to users, most platforms allow individuals to communicate with other users within a user-defined network. Twitter, for example, allows users to send messages (tweets) of up to 140 characters to other users (organizations, governments, or individuals), hashtags (short words or phrases beginning with a pound
S. C. Vos and M. M. Buckner sign that function like discussion boards), and followers (other Twitter users who receive message updates directly from another user’s account). The Twitter interface encourages users to follow people with whom they may or may not have an interpersonal relationship. These relationships are not necessarily reciprocal: One Twitter user may follow a second Twitter user, but the second Twitter user may not follow the first Twitter user. Users broadcast information to their followers, and the service encourages users to retweet or to pass on messages they receive to their followers. Since the service first went live in 2006, Twitter has grown to 284 million active users worldwide (Twitter, 2015). Previous examinations of social media use during health crises have tended to focus on disease tracking (Chew & Eysenbach, 2010; Signorini, Segre, & Polgreen, 2011). For example, during the 2009 H1N1 flu pandemic, Chew and Eysenbach (2010) conducted a content analysis of 5,395 tweets related to the pandemic. The purpose of the study was to demonstrate that Twitter could be used to track an epidemic, specifically attitudes and behaviors about the epidemic. The researchers found that more than half of the tweets communicated news and information about the virus, and 22.5% of tweets communicated a personal experience. The vast majority of tweets that provided information contained a link to a reference that individuals could follow to validate the information in the tweet. However, those references rarely led to traditional public health and government authorities like the World Health Organization (WHO) or the CDC. Signorini and colleagues (2011) also examined chatter about the 2009 H1N1 outbreak on Twitter. Their analysis focused on public sentiment in response to the outbreak and disease activity. Neither study assessed whether the messages contained content that could help the public (a) engage in sensemaking or (b) respond appropriately to the crisis. Since the H1N1 outbreak in 2009, Twitter use has increased rapidly in the United States (Duggan & Brenner, 2013) and across the globe (McCue, 2013). Because of the increased popularity of social media and, in particular, Twitter, official organizations are using the medium as a way to communicate with a large, general audience. Both the WHO and the CDC maintain Twitter accounts and use these accounts to tweet about potential public health crises. In April 2013, the WHO tweeted regularly to its 789,000 followers about H7N9, sending out efficacy messages and updates on the number of cases and deaths (WHO, Twitter page, April 26, 2013). The CDC maintains several Twitter accounts, including one dedicated to flu information, @CDCFlu. In April 2013, the CDC flu account provided information about H7N9 to its 246,000 followers. By communicating via Twitter, the WHO and CDC can quickly spread information to a large number of followers. Research suggests a positive relationship between the number of followers and the reach of a Twitter account’s messages (Suh, Hong, Pirolli, & Chi, 2010). However, the impact of these organizations on the public conversation about health-related issues on social media platforms has not been examined.
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Tweeting Bird Flu Based on this review, this study seeks to answer the following research questions: Research Question 1: During an emerging public health crisis, do messages delivered via social media contain content that would allow individuals to make sense of what is happening?
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Research Question 2: During an emerging public health crisis, do messages delivered via social media contain content that would allow individuals to develop efficacy, should the event transform into a crisis?
Methods The 2013 outbreak of the H7N9 virus, a variant of the bird flu, was chosen to examine social media messages in the emerging stages of a risk and crisis event. Although not a full-blown crisis, the outbreak had the potential to evolve into a pandemic, as defined by the CDC (Reynolds & Quinn, 2008): (a) The H7N9 virus was novel, meaning that humans had not been exposed to it before and therefore would have little immunity to it; (b) the virus was deadly; and (c) at the time of data collection, it appeared to be spreading (Bradsher, 2013b). The Chinese government announced the discovery of the virus on March 31 (Bradsher, 2013b). Within 10 days, the virus had infected 24 people and killed seven (Bradsher, 2013a). On Twitter, chatter about the H7N9 virus quickly reached thousands of ‘‘relevant’’1 tweets per day (Topsy Pro Analytics, 2013). In this study, we analyzed 25,598 unique tweets about the virus produced between April 10 and April 18. Data Collection Starting April 17, 2013, tweets were gathered using Twitter’s application programmer’s interface. At the time, the service gave access to a subset of the tweets produced at any given time and allowed tweets to be gathered up to 7 days in the past. A total of 25,598 unique tweets were collected using four sets of search terms: H7N9, avian and flu, bird and flu, and China and flu. The 25,598 tweets were produced by 14,430 unique Twitter accounts. Individual accounts contributed between 1 and 163 tweets in the data set, with most accounts contributing one tweet (Mo ¼ 1, Mdn ¼ 1, M ¼ 1.77) to the data set. Four accounts contributed more than 90 tweets each to the data set: @AngryPigss (163 tweets; unidentified account), @Crof (123 tweets; account identified as a retired Canadian college teacher and writer), @ah1n1news (104 tweets; account identified as Andrew, a self-proclaimed H1N1 journalist from Arizona), and @ironorehopper (93 tweets; account identified as a multilingual blogger). 1
According to Topsy Pro, relevant tweets are those that include a link or have been retweeted or passed on to another user. Their models do not include tweets that (a) are not retweeted or (b) do not contain a link.
Most of the sample consisted of original tweets. Approximately one third of the tweets were identified as retweets (34.1%, n ¼ 8,720). Approximately three fourths contained hyperlinks (78.3%, n ¼ 20,045). Analysis of the Tweets The coding scheme was developed using the CERC framework (Reynolds & Quinn, 2008; Reynolds & Seeger, 2005) and a pilot coding of 3,000 tweets that were randomly selected from the total. The scheme assessed the type of information communicated. In order to be included in a category, the information had to be present in the text of the tweet, or the tweet had to clearly indicate that the information was available in the link provided. It was beyond the scope of this study to examine the content of links. Intercoder Reliability Intercoder reliability was assessed using a two-stage process. In the first stage, the reliability of the coding scheme itself was established using a manual coding process (Krippendorff, 2004). After a brief training session, we manually coded 200 tweets. Krippendorff’s alpha was used to measure intercoder reliability, using the SPSS macro created by Hayes and Krippendorff (2007). Coder agreement exhibited strong reliability (a > .7; Krippendorff, 2004) or a high percent agreement (>90%). Once reliability was established for the manual coding, an automated coding scheme was developed using KH Coder (Higuchi, 2013), a software that provides the capability to develop complex codes based on individual words, word combinations, words in proximity, and and-not combinations (e.g., ‘‘viral’’ and ‘‘not microblog’’). The second stage of reliability assessment established that the automated coding scheme reliably coded tweets compared with the manual coding. Using an iterative process, we assessed the automated coding by examining tweets in each coding category, tweets that did not code in any category, tweets that coded in more than one category, and random selections of 1% of the data set. These checks were used to refine the automated coding until it coded with a high degree of reliability compared to manual coding, as assessed by Krippendorff’s alpha (a > .899; Krippendorff, 2004). Measures Each tweet was coded for two types of information: sensemaking and efficacy. The categories were distinctive but not exclusive. Tweets were coded for each category separately; individual tweets could be coded for more than one category. Sensemaking. Tweets that contained content that could allow individuals to make sense of the emerging crisis were categorized as sensemaking tweets. Following Weick’s (1995) description of the sensemaking processes, these tweets contained information that showed individuals placing events into frameworks, comprehending what was happening, accommodating the unexpected, interacting to produce understanding, and identifying the pattern of events. Sensemaking tweets contained information about the number of people infected, the number of deaths, the spread of
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304 the virus, vaccine development, and the likelihood of human-to-human transmission. Tweets also included nonfactual information and evaluations of the severity of the crisis. Intercoder reliability for sensemaking was high (manual coding, Krippendorff’s a ¼ .078; computer coding, Krippendorff’s a ¼ .09; Krippendorff, 2004). Efficacy. The category of efficacy captured tweets that provided information that could encourage self-efficacy and response efficacy (Reynolds, 2006; Reynolds & Quinn, 2008). Information provided in these tweets included basic disease control instruction (e.g., frequent hand washing, cough covering), cooking instructions (e.g., preparing chicken), and travel advice or restrictions. The two coders established a high percent agreement (90.5%); however, intercoder reliability for the manual coding of efficacy was low (a ¼ .3; Krippendorff, 2004). This may have been because of the small number of tweets (6=200) in the sample that contained efficacy information. The two coders resolved disagreements through discussion. Computer coding exhibited a high level of agreement with the manual coding (a ¼ .1; Krippendorff, 2004). Following the establishment of reliability, a computer analysis of all 25,598 tweets was conducted. The coded tweets were then pulled into SPSS for analysis.
Results A large proportion of the analyzed tweets contained sensemaking information about the virus (n ¼ 22,612, 88.3%). Sensemaking tweets placed the crisis in a framework, provided details of cases and deaths, demonstrated how individuals were accommodating the unexpected, identified patterns in the crisis, and suggested interaction. Most of the sensemaking tweets contained hyperlinks (n ¼ 18,081, 80%). Tweets placed the emerging crisis in a framework by associating the virus with other negative events. One tweet exclaimed, ‘‘Bird flu will wipe out China, obesity will wipe out USA and mad cow disease will take kut UK. Korea will just get nuked.’’2 Other tweets placed the event in a framework by dismissing it as a media frenzy: ‘‘Bird flu pandemic from China and nukes in North Korea; nothing like some fear mongering from the media.’’ (See Table 1 for additional examples of each tweet type.) Other tweets allowed people to comprehend what was happening by reporting details of the crisis. Tweets provided updates about the number of cases of H7N9: ‘‘Record Number of 14 H7N9 Cases Reported Today 15 H7N9 cases have been reported . . . ’’ Tweets also provided information about the number of deaths: ‘‘Bird flu death toll in China rises to 14.. . .’’ In addition, tweets included information about how the virus was spread: ‘‘RT @HaertlG: We are exploring the possibility that #H7N9 can be spread between people; as yet there is no evidence of sustained H2H transmission.’’ Sensemaking tweets showed individuals accommodating the unexpected through exclamations and reactions. ‘‘Oh my god!!’’ tweeted one user, ‘‘Bird flu started again n china. 2
Tweets are reproduced verbatim. As a result, they may contain typos.
S. C. Vos and M. M. Buckner Can’t travel any country these days. Everywhere there’s some issues: (.Better stay home:P.’’ In another tweet, a user remarked, ‘‘You telling me I’m going to have to deal with the West Nile & the muthafuckin Bird Flu this summer? crotch grab #ItsHardOutHereForAPimp.’’ In addition, tweets showed individuals identifying patterns in the emerging crisis. One user tweeted, ‘‘Pattermomg @live_h1n1 This isn’t really a surprise. Wrote about this a few years back on #H5N1 http://t.co/7nV4bhyVc2 Still relevant to #H7N9.’’ Another user tweeted, ‘‘The world is becoming more and more wild with daily serious events incidents taking place such as H7N9 Boston bombings Inmates escape.’’ Tweets also contained content that suggested interaction. One user tweeted, ‘‘Keep an eye on this one: Report on 3 in China Who Died From Bird Flu Points to Severity of Strain http://t.co/jb4NXaEYAX.’’ Another user tweeted, ‘‘A few comments about #H7N9 in #China 1) We still don’t know much. 2) To date 33 known cases & 9 deaths http: //t.co/aYslVd8MgU–>#fb.’’ Less than 2% (n ¼ 423) of the sample tweets contained efficacy information. The majority of these tweets included hyperlinks (n ¼ 254, 60%). These tweets included information about the lack of a travel restriction to China. For example: ‘‘RT @CDCtravel: #H7N9 fact: there’s currently NO recommendation against travel to China.. . .’’ They provided advice about airport screening: ‘‘I follow @19AlexG ! Flu screening at airports best done prior to travel: Screening people leaving a country . . . ’’ Efficacy tweets emphasized the importance of eating well-cooked meat: ‘‘RT @WHO: You wont become infected with #flu virus through eating well-cooked meat & food incl. chicken, other poultry products #foodsafety #H7N9.’’ Other tweets in this category encouraged travelers to avoid animals: ‘‘Dr. Obvious from CDC gives advice to travelers: Do not touch pigs in China http://t.co/34y2p2MIvd.’’ It is noteworthy that some of the advice provided in efficacy tweets was not necessarily supported by scientific evidence. One user tweeted, ‘‘How to avoid getting H7N9 virus: Be a vegetarian from now on.’’ A small number of tweets contained both sensemaking information and efficacy information (n ¼ 154, 0.6%). For example, one user tweeted, ‘‘Hoping everyone can wash their hands carefully and has less contacting with chickens and birds, the H7N9 flu is crazy in China!’’ Another tweeted, ‘‘Wash your hands! Deaths from new bird flu underscore grim fears, reports show http://t.co/aBJEIjoCBP via @NBCNewsHealth.’’ Approximately 11% (n ¼ 2,717) of the tweets did not reflect sensemaking or efficacy information. These tweets did not contain enough information to assess how they contributed to sensemaking or efficacy. Most of these tweets contained hyperlinks (n ¼ 1,842, 67.8%). For example, tweets that mentioned the virus but did not add additional information were not coded for either category: ‘‘Epidemiological update of 15 April: avian influenza A(H7N9) virus in China . . . http://t.co/KUijabAycY.’’ Other tweets in this category included clearly spurious claims of having the bird flu: ‘‘@davefulll tellmrs gallianagh I have bird flu and cant do my orals.’’ These tweets also included references to the bird flu that were meant as jokes but did not contain
Tweeting Bird Flu
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Table 1. Examples of tweets Sensemaking (n = 22,612, or 88.3%) Placing the crisis in a framework . I feel like health epidemics like bird flu are just ways to stimulate an Economy . @ChaoxLoL Hey, Chaox. I’m in Shanghai right now and I just wanted to let you know that the Bird Flu is not really a big deal. Media overhype . And this new H7N9 is believed to now jump from human to human. Fuck This is contagion Comprehending what is happening .:O @STcom: #China reports new #H7N9 bird flu death, 2 new infections http://t.co/HXMADDxMwY . RT @CDCFlu Ongoing community spread is needed for a pandemic to start that has not been seen in China with #H7N9 . According to @WHO, so far there are 38 confirmed cases infected with new strain H7N9 in China, 10 deaths and 19 severe. eldely predominant. Accommodating the unexpected . bird flu is back wtf . RT @nigelcameron: Please people, be scared. It’s comimg. RT @joergheber: 5 new confirmed RT @WHO: Following #H7N9? http://t.co/PcsU5MTMI7#flu#risk . RT @princebhojwani: Woah, that was fast. RT @BloombergNews: China confirms 77 H7N9 infections, 16 deaths: Xinhua Interacting . Worth reading. Latest on bird flu in china. http://t.co/1twppwC8JO . @setiogi Thanks for the #H7N9 updates! My grandpa had the 1918 #SpanishFlu as a baby this is a story I care about. Glad to be informed! . RT @WHO: #H7N9 in China: We will be watching for signs of human-to-human transmission and new cases in other geographical areas. #influenza Identifying patterns: . The world is becoming more and more wild with daily serious events incidents taking place such as H7N9 Boston bombings Inmates escape . Whenever I start my deadly pandemics in Plague Inc., I always start it in China. #H7N9 . #birdflu hits the UK. Will this turn out to be #h7n9? Hopefully not. http://t.co/QxYvsyFwtw Efficacy (n = 423, or 1.7%) . CDC offers H7N9 avian flu advice for travellers and expats in China: http://t.co/xaLDtEBtdT . RT @WHO: Prevent #H7N9: Wash hands, utensils & chopping board after handling raw meat, newly slaughtered animals, esp.poultry #foodsafety . @adamlambert be extra cautious in china. Bring along hand sanitizer and dun eat unhygienic food! Be careful of bird flu! And have fun! Sensemaking and Efficacy (n = 154, or 0.6%) . @hcfischer1 Would you rather die from bird flu or a nuclear strike? Caution around birds and eating eggs=poultry always a good idea in SEA. . Best #F1Shanghai advice from @raerity: Not the trip 2 be adventurous w= food: 24 avian flu deaths, 16 K pigs in the river. Eat clean & wise. . Bird is on the way . . . so Make sure all of you people Spread the Word! THE BIRD FLU IS COMING! So make sure too wash your hands 7xs a Day Other (n = 2,717, or 10.6%) . @BarryTuck take ACC200 effervescent tablets. Makes chicken soup look like a some weird lovechild of Avian and Swine flu. . ‘‘I go in there, I get bird flu, I don’t come out . . . ’’ #30rock . Your tweets are giving people the bird flu, please STFU
information that would allow for sensemaking or efficacy about the virus itself. One joke was repeated by several different users: ‘‘Whats the difference between bird flu and swine flu?. For bird flu you need TWEETMENT, for swine flu you need OINK-MENT. Ill shutup na.’’
Discussion Social Media as a Medium for Sensemaking The results of this study suggest that social media provides a forum where individuals can make sense of an emerging
crisis, fulfilling an important need as outlined by CERC (Veil et al., 2008). Over the 9-day study period, messages distributed via Twitter contained content that would allow for sensemaking using the processes outlined by Weick (1995). Tweets provided information that placed the emerging crisis into a framework. Tweets contained information that would allow individuals to comprehend what was happening by reporting on the number cases and the spread of the virus. Tweets showed individuals accommodating the unexpected and interacting to produce understanding, and tweets identified patterns.
306 Taken together, these messages had the potential to help individuals to make sense of the emerging crisis. From the messages, individuals could have concluded that a serious virus with pandemic potential had emerged in China. However, the messages also communicated that the virus was not spreading easily from individual to individual and that officials were monitoring the situation. Although some of the tweets contained misinformation, the information communicated in the sensemaking tweets mirrored much of the information contained in the official report written by the WHO (2013a) after the outbreak ended.
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Designing Efficacy Messages for Social Media The results suggest that Twitter did not provide a forum for efficacy information. The low proportion of tweets communicating information that could enhance efficacy is troubling, as research suggests that efficacy information is critical to encouraging appropriate response (Reynolds & Seeger, 2005). The small proportion of efficacy tweets may be the result of the emerging nature of the crisis and the geographical distance of the event for many English-language Twitter users. At the time these tweets were collected, the virus had not spread outside of China, and it never did (WHO, 2013b). This study was not able to assess tweets in languages other than English, and it is possible that efficacy information was disseminated in other languages via Twitter or on another social media platform (e.g., the Chinese platform Sino Weibo). However, the WHO and CDC, two major organizations in global public health, tweeted about the crisis in English and provided efficacy messages in English. In addition, CERC recommends that efficacy messages encourage appropriate response and a belief that the response will work (Veil et al., 2008). During the time period of this study, four to six new patients were identified every day, and people were dying (WHO, 2013b). In addition, no one knew how the virus was spreading (CDC, 2013a). Although this event never transitioned into a pandemic, the event had pandemic potential (CDC, 2013b). The study did find some messages that discussed washing hands and eating only fully cooked poultry. Yet many of the behaviors recommended by the CDC (2013a) specific to this outbreak as well as behaviors recommended for a more general outbreak (Reynolds, 2006) did not appear in the data set. In addition, the messages did not include any information that would encourage response efficacy (Coombs, 2009), the belief that the recommended measures would work. These findings are particularly troubling as research suggests that individuals do not understand the role they play in preventing the spread of viruses with pandemic potential (Elledge, Brand, Regens, & Boatright, 2008). Elledge and colleagues (2008) found that individuals did not understand the effectiveness of nonvaccine control measures like voluntary quarantines that would be necessary if a virus reached the pandemic stage. Although tweeting all of the strategies the CDC has identified for infection control at an initial
S. C. Vos and M. M. Buckner crisis stage may not be necessary, the CERC framework suggests that certain messages would be appropriate (Reynolds, 2006). For example, messages could promote washing hands, covering coughs, and maintaining social distance (e.g., staying home from work, not traveling) when experiencing symptoms. Messages might also promote response efficacy and educate individuals that nonvaccine control measures work. In the case of an avian flu, messages should also promote not touching live or dead animals when traveling and eating only food that is fully cooked. Although several of these message types appeared in the data set, less than 2% of the tweets collected contained efficacy information. Thus, efficacy messages had a low presence in the public discourse about H7N9 on Twitter. There are several possible reasons why this occurred. One possibility is that the WHO and the CDC were not tweeting enough efficacy messages to have a substantial influence on the Twitter conversation about H7N9. At least one study has shown a lack of efficacy messages in the social media response of official organizations. Sutton, League, Sellnow, and Sellnow (2015) found that in a public health crisis that involved a flood and water contamination, a low proportion of messages sent by governmental organizations contained efficacy information. Another possibility is that there were few efficacy messages because of the networked nature of social media platforms. Even if the CDC and the WHO were tweeting efficacy messages, those messages would only have a large reach if their followers were retweeting those messages. For many English-language Twitter users, messages about a far-off, emerging crisis may not have been relevant enough to encourage the frequency of retweeting that would have resulted in a substantial proportion of efficacy messages. However, CERC suggests that efficacy information be distributed before a virus has reached a pandemic stage (Reynolds, 2006). As a practical matter, this means that officials need to distribute efficacy messages before they are relevant to individuals. By the time a pandemic virus has spread far enough for efficacy messages to be relevant to more users, the messages may be too late. This suggests that researchers and practitioners need to understand how to compose social media messages in a manner that would encourage redistribution through a networked platform, even if those messages lack strong relevance. In other words, researchers and practitioners need to design messages for social media to encourage distribution throughout the network. These messages should draw on existing research about social media messages and factors that increase the redistribution of messages. For example, a large proportion of the efficacy messages contained a hyperlink to an Internet website. Existing research suggests that the inclusion of a hyperlink reduces the reach of a message by discouraging (or at least not encouraging) the redistribution of a message through a network in other contexts (Malhotra, Malhotra, & See, 2012; Sutton et al., 2013). In addition, when message designers depend on the contents of a Web page to convey information (e.g., ‘‘CDC offers H7N9 avian flu advice for
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Tweeting Bird Flu travellers and expats in China: http://t.co/xaLDtEBtdT’’), they lose an opportunity to provide immediate, actionable information: Not everyone who sees the tweet will click on the hyperlink. Although these choices may seem small, they influence who is exposed to a message and how many people are exposed to the message. Research suggests that the more often people are exposed to a message encouraging protective action, the more likely they are to respond appropriately (Wood et al., 2012). More work needs to be done to understand what message elements contribute to redistribution on social media and how social media platforms can be used to encourage redistribution. These results suggest that in addition to distributing efficacy messages prior to a pandemic, practitioners need to distribute more efficacy messages via social media during future potential outbreaks. These messages should contain more, specific information about how to protect oneself against exposure, and the messages should encourage response efficacy, in particularly the belief that nonvaccine infection-control measures are effective (Reynolds & Quinn, 2008). Limitations and Future Research By only using tweets to assess information communicated about H7N9, this study does not account for information spread through other social or traditional media channels. This study also included a limited time frame. In addition, the purpose of this study was to assess the public discussion of H7N9, and thus the study does not provide an analysis of what any one individual Twitter user was exposed to. However, this study does assess how an emerging public health crisis is discussed on a social media platform. In light of the study’s findings, future research should explore the lack of self-efficacy messages present in social media discourse about health crises, consider the accuracy of information shared, and examine whether message design could encourage wider dissemination of efficacy messages on social media. Most notable is that scholars should examine whether the lack of efficacy messages is unique to a potential pandemic crisis or also exists in other social media discussions of health-related crises. Though beyond the scope of this study, examining the accuracy of sensemaking and efficacy tweets may inform scholars and practitioners about the quality of information disseminated during health crises. In addition, future research should examine the distribution of efficacy messages from organizations like the WHO and CDC in order to assess retweet frequency and how specific message elements relate to wider tweet dissemination. Such a study might allow officials to design messages that are more likely to be retweeted and thus more likely to be spread to large numbers of users.
Conclusion When a crisis emerges, a key way people obtain and send information is through social media (Faustino et al., 2012). At the same time, health-related crises create situations in which officials need to communicate effectively and rapidly
through many communication channels (Ulmer, Sellnow, & Seeger, 2011). Two key types of information that need to be communicated are sensemaking and efficacy messages (Veil et al., 2008). This study demonstrated that in the emerging phases of a health-related crisis, the type of content distributed through social media allowed for sensemaking. However, this study found that few tweets contained efficacy information, and as a result social media was not an effective platform for distributing information that could contribute to appropriate response.
Acknowledgments We thank Timothy Sellnow, PhD, and Zixiu Tai, PhD, at the University of Kentucky for their comments on earlier versions of this article.
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