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Steinberg, Wukich, and Wu: Central Social Media Actors

International Journal of Mass Emergencies and Disasters March 2016, Vol. 34, No. 1, pp. 47-74.

Central Social Media Actors in Disaster Information Networks

Alan Steinberg Center for Civic Leadership Rice University

Clayton Wukich Department of Political Science Sam Houston State University Email: [email protected]

Hao-Che Wu Department of Political Science Oklahoma State University

Government agencies and media outlets traditionally have played central roles in disaster information networks. However, their hub status has slightly diminished as social media empowers others to communicate more easily. Little systemic research has identified the key actors in these new information networks. This paper examines Twitter hashtag networks during four disasters, the Boston Marathon bombing; the West, Texas fertilizer plant explosion; the Midwest spring flooding in Peoria, Illinois; and the Moore, Oklahoma tornado. Findings indicate minimal government and nonprofit involvement. While traditional media outlets still played an important role in some networks, others were more heavily shaped by influential private citizens. We examine those networks by identifying key actors and evaluating their message content and conclude by offering an initial typology for hashtag information networks and discuss directions for future research.

Keywords: Social media, Twitter, information networks

Introduction

Disasters often disrupt traditional modes of communication resulting in information asymmetries that develop in which some people and organizations receive relevant information while others do not; a factor that leads to suboptimal disaster outcomes

Steinberg, Wukich, and Wu: Central Social Media Actors

(Comfort 2007a). People have long relied on their social networks and traditional media sources (e.g., television news, newspapers, and the radio) to bridge these gaps and inform their protective action decisions to reduce their exposure to risk (King 2004; Lindell and Perry 2012; Mileti 1999; Nigg 1995; Paul et al. 2003; Perez-Lugo 2001; Wijkman and Timberlack 1998). Today those impacted increasingly turn not just to their physical neighbors and the traditional media for more information, but also to their online social networks (American Red Cross 2012). Social media platforms such as Twitter and Facebook provide opportunities for people to search for and exchange disaster-related information. What those people receive may not be from official sources, but instead may have been sent by a mix of organizations they follow, their friends, friends of friends, or complete strangers. Twitter, for example, facilitates open, accessible information exchange about specific, sometimes emergent topics through its trending topics and hashtag architecture, and anyone can participate who has internet access or by using Twitter text messaging. While research increasingly addresses the extent to which people use social media (Duggan and Smith 2013), less is known about who participates in these networks, who the key actors are, and the types of messages they disseminate. In social media information networks, the voices of government and nonprofits do not appear as prevalent as in traditional models of crisis communication in which hub status is shared with traditional media outlets (Sutton 2010; Vultee and Vultee 2011; Wukich and Steinberg 2014). Key network positions are therefore held by other actors. This article examines those unofficial actors by exploring hashtag networks on Twitter. Evidence is evaluated across four cases that occurred during April and May of 2013: The Boston Marathon bombing; the West, Texas fertilizer plant explosion; the Midwest spring flooding in Peoria, Illinois; and the Moore, Oklahoma tornado. Findings indicate that key actor types include traditional and new media outlets as well as celebrities, such as television personalities Kerry Washington and Eliza Dushku who enjoy large followings. We suggest a typology of disaster-information networks in which networks can be media- or citizen-centered and provide different types of information that may be more or less valuable depending on the particular information seeker.

Changing Environment of Information Exchange during Disasters

As technology changes, opportunities for people to search for and exchange information have increased. The diffusion of social media has led to new ways for information to be shared as well as providing for a new means for information to be absorbed (Mergel 2012; Southwell 2013). However, there is much we still do not know about how social media technology is used, especially during disasters. While researchers have examined how government agencies use the technology and structure message content across both public safety (Kavanaugh et al. 2012; Meijer 2014) and emergency management (Hughes et al. 2014; Sutton et al. 2014; Wukich and Mergel 2015), recent research suggests that government agencies and other official organizations play only minor roles in online social media-driven disaster networks

Steinberg, Wukich, and Wu: Central Social Media Actors

(Helsloot and Groenendaal 2013; Wukich and Steinberg 2013). Several studies have explored how individuals use social media during extreme events (Bruns and Stieglitz 2012; Hughes and Palen 2009; Vultee and Vultee 2011). Less research has been done on the information networks that emerge between actors and the composition of these networks (see Sutton 2010; Sutton et al. 2013; Wukich and Steinberg 2013) despite the fact that people increasingly turn to social media for disaster-related information (American Red Cross 2012). While the term social media can have varying definitions, the focus in our research is on specific online social networking tools that facilitate information exchange, specifically Twitter. Twitter can be used to distribute important information during disasters by both official channels (e.g., the National Hurricane Center, the National Weather Service, and the U.S. Geological Survey) and unofficial channels (citizens and organizations not charged with disaster response, but who converge on the topic). Information receivers who once could only pass along information to a narrow audience through traditional modes of communication, such as phone and face-to-face conversations, can now serve as channels to distribute useful information about environmental threats or protective actions to a much wider audience (Lindell and Perry 2012). Research suggests that many people rely on social media through their computers, tablets or smartphones, to access and share information during disasters (Hughes and Palen 2009; Sutton, Palen, and Shklovski 2008). A recent poll shows that social media users were more likely to seek out and exchange information as compared to non-users, and 76 percent of those surveyed indicated that they would contact friends via social media during a disaster (American Red Cross 2012). The proliferation of social media has not necessarily revolutionized how people make protective action decisions because basic cognitive processes are still used. People must first recognize and interpret personal risk and then develop strategies to reduce that risk. Unfortunately, complete information is rare, especially during conditions of particular uncertainty. Theorists suggest that complete information is almost impossible (Klein 1998; Weick and Sutcliffe 2007) as asymmetries deny people pertinent information (Comfort 2007b). While complete information does not necessarily lead to better decisions, it increases the likelihood that more informed choices will be made (Lindblom 1990). Fortunately, advanced information and communication technologies have made it possible for people to receive more information than they could previously. Accounting for multiple sources including social media, Lindell and Perry (2012) proposed a modified Protective Action Decision Model (PADM) to illustrate risk communication. According to the model, information sources include environmental cues, social cues, socially transmitted warnings, and information channels, any of which can inform protective action decision making (Lindell and Perry 2012; Wu, Lindell, and Prater 2013a). During this information transmission process, the social networks involved are very important. The availably of risk information and its interpretation often depend on the receivers’ warning information network, which include factors such as the relevance, accuracy, and timeliness of circulating information and the

Steinberg, Wukich, and Wu: Central Social Media Actors judgment brought to bear in the sense making process (Lindell and Perry 2004, 2012; Lindell, Prater, and Peacock 2007; Mileti 1975). Some information receivers are able to obtain adequate levels of information because of their comprehensive list of warning sources; they receive information from their peers, neighbors, traditional media, internet, and other personal contacts. However, some information receivers have limited information access, and as a result, asymmetries during a disaster event result. Media outlets reduce information asymmetry and speed up the transmission of information. Theoretically, little has changed in the sense that people still rely on the media and their personal networks. Now, however, people expect information instantaneously (Zavattaro and Sementelli 2014); interested individuals are more likely to seek out information rather than waiting (American Red Cross 2012). This is because the sources of disaster information have changed over the last decade. Although a sizeable proportion of the population still use the traditional news media as their primary information source, these users tend to be older (Lin et al. 2013, Siebeneck and Cova 2014). Younger individuals increasingly use online sources (American Red Cross 2012; Rodriguez, Diaz, and Donner 2005). Some of these information consumers, including emergency managers, use the internet and relevant social media tools, especially Facebook, Twitter, and blogs, as their primary sources to assess disaster/weather information (Liu, Palen, and Giaccardi 2012; Palen et al. 2010; Rodriguez et al. 2005). The public, in general, are beginning to rely on the internet as their disaster-related information source, since disaster and weather information distributers such as the National Hurricane Center, the National Weather Services, and the United States Geological Survey increasingly use web-based tools to disseminate specific disaster- related data directly to the general public (National Research Council 1999; Rodriguez et al. 2005; Wu, Lindell, and Prater 2013b). Rather than waiting for a newspaper or a television report, people are increasingly searching the internet for more timely and specific information (Taibi 2013). In the case of social media, people depend on online connections (e.g., online social media contacts or resources) rather than looking to their neighbors next door for this information. This is in line with Raine and Wellman’s (2012) notion of networked individualism in which people increasingly seek out information and prefer to find it online rather than seeking out information face-to-face. The question of which online actors garner the most attention is yet to be systemically examined. Sutton (2010) illustrated how Twitter facilitated a discussion between geographical-dispersed actors despite the lack of national media attention during an environmental disaster and reported that environmental activists and both traditional and new journalists played important roles in bringing attention to the incident. The study examined just one case, though, and did not illustrate the relationships that emerged between actors. Neither did Vultee and Vultee (2011), although they illustrated the composition of several Twitter information networks by user group and message content. Wukich and Steinberg (2013) employed social network analysis to evaluate several Twitter-based information networks. Using centrality statistics, they determined that 50

Steinberg, Wukich, and Wu: Central Social Media Actors government and nonprofit agencies play a surprisingly small role in the overall diffusion of disaster-related messages, the assumption being that central network figures will have a larger impact on the flow of information and other resources within a system (see Comfort and Haase 2006). Wukich and Steinberg (2013), however, did not systemically examine the actors that supplanted theses agencies as key actors. Centrality is relevant to information diffusion not just in disaster situations, but in general, since central actors possess a greater number of opportunities to influence other people (Krackhardt and Hanson 1996). Marketing research, for example, finds that individuals wait for key early adopters to evaluate a product before they make purchasing decisions, thus relying on a central set of opinion makers (Goldenberg, Libai, and Muller 2010). Indeed, in terms of network architecture, a core set of actors quite often demonstrate disproportionately higher centrality scores; therefore, other actors operating on the periphery may depend on these central actors for information (Barabási 2002). It is important to note, however, that centrality alone is not necessarily a determining factor regarding the diffusion of key pieces of information. Watts and Dodd (2007), for example, empirically demonstrated that information flow within various networks depends not just on a few central actors but also on the receptivity and willingness of their peers to accept and pass along that information.

Using Twitter Hashtags to Categorize and Make Sense of Information

Increasingly, private citizens and organizations have the opportunity to disseminate disaster-related information online. However, information abundance may in itself be a problem because the flow of information online can be too extensive for people to sort through, and valuable data may get lost among the vast amounts of information in a data stream (Latonero and Shklovski 2011). Both the number of participants and the sheer volume of information can be overwhelming. For example, Huang, Chan, and Hyder (2010) noted that Taiwanese residents experienced difficulties validating the abundance of risk information during the 2009 Typhoon Morakot. There are online tools that can help to deal with this problem (Vieweg, Castillo, and Imran 2014) and a growing number of strategies and tactics for individuals to aggregate disaster information and attempt to differentiate the signal from the noise. Crowdsourcing platforms such as Ushahidi and Google Person Finder enable individuals to share, aggregate, and analyze important pieces of information (e.g, hazard impact, the availability of key resources, and the location of loved ones) (Haddow and Haddow 2014; Liu and Palen 2010). Twitter’s trending topics and hashtag functions facilitate this type of information exchange as well as do TweetDeck and other social media monitoring tools. Through the use of Twitter’s trending topics platform, multiple actors who have had no prior connections with one another can identify topics that are receiving considerable interest online and share and respond using the same Twitter hashtag to group and broadcast their messages. Additionally, hashtags can be used as a means for individuals to self-organize their messages into searchable categories. This same tool allows

Steinberg, Wukich, and Wu: Central Social Media Actors individuals to participate as part of a self-organized community by including hashtags within their message traffic (Wukich and Steinberg 2013).1 These hashtags are thus a community-driven convention that allow context to be added to messages via metadata and provide a way to create networks of information (Leaman 2009). Twitter users can adopt established hashtags or create new categories (Bruns and Burgess 2012; Bruns and Stieglitz 2012). The first use of hashtags on Twitter occurred in August 2007 when programmer Chris Messina suggested using the pound symbol to organize channels of information within Twitter (Parker 2011), and the practice quickly gained traction. Shortly after, Nate Ritter, a Southern California resident, used the hashtag #sandiegofire in October 2007 during a severe suburban wildfire; this proved to be the first hashtag use during a disaster. The use of the hashtag helped to create an information network made up of help requests, alerts and warnings, and other information relevant to an individual’s protective action decisions (Messina 2007; NewSquared 2008). The first disaster hashtag demonstrated how one person without an official governmental position could create a wide impact by informing other affected citizens. Hashtags continue to be used during disasters (Olteanu, Vieweg, and Castillo 2015). Not all uses of hashtags are equally important, though, since they can vary greatly in content, length of use within a community, and amount of use among Twitter users. Any user can employ a hashtag to classify their individual tweet, leading many hashtags to be nothing more than a collection of people’s thoughts on a given topic. Hashtags are not the only means of creating an information network; messages can also be shared among an individual’s own social network through the act of retweeting.2 During disasters, some users retweet messages from other sources, indicating a desire to share critical information with other people within their social network (Bruns and Stieglitz 2012). Paying attention to and sharing the messages of others help to create a more interconnected network, for high volumes of response messages may indicate users are not just tweeting into the hashtag stream, but are following the conversation. As a greater number of participants use a given hashtag, the more the network starts to become a community of individuals sharing information (Bruns and Burgess 2012). Larger, self-organizing information networks are thus important tools for the dissemination of information critical during an extreme event; they can provide users with critical information necessary for deciding upon appropriate action and to support more resilient communities. However, users of Twitter are not limited to individuals; Hermida (2010) reported that media outlets such as television stations, newspaper publishers, and radio stations regularly use Twitter also.

Hypotheses

Based on past research and evidence from practice, our research investigates the following hypotheses:

Steinberg, Wukich, and Wu: Central Social Media Actors

H1: Individual users (i.e. private citizens) will constitute a greater percentage of information networks than will government or nonprofit actors. With millions of users, the majority of whom are private citizens, it is logical to assume that many more individuals will participate in disaster-related information networks than organizations. While many organizations involved in disaster response are using social media, individuals have played significant roles in populating and facilitating these networks (Sutton 2010; Sutton et al. 2008). Additionally, Wukich and Steinberg (2013) found that despite increasing social media adoption, government and nonprofit actors still play a limited role within Twitter hashtag communities in terms of occupying central roles.

H2: Media actors will be paramount in distributing information; having the highest centrality scores within each network.

Given that we expect to find limited involvement by emergency management agencies in the actual information networks, we believe that media actors, users representing established mass media outlets, will play central roles within these networks acting as quasi-official and reliable sources of information.3 Public sector organizations lag behind other entities in terms of social media innovation as well as preexisting followings. We assume that the dissemination of public information still relies on traditional media as it has for decades albeit via a new platform (Boin et al. 2005; Quarantelli 1996). Traditional news media messages are more likely to be amplified by individuals, considering their large, preexisting followings and their greater access to valuable public sector information. Furthermore, traditional media outlets are more likely to demonstrate high centrality given their perceived trustworthiness.

H3: Central actors will not be limited to the local jurisdictions the disasters take place in.

With respect to large-scale disasters that receive national media attention, we expect the majority of Twitter networks to be comprised of many nonlocal participations– individuals not from the affected area. Other studies have noted that the users of hashtag networks tend to be unaffiliated individuals, those not directly affected by the event (Heverin and Zach 2012; Sutton 2010). Locals will still participate–the vast majority are likely to be information consumers, scanning the hashtag categorization for desired information and occasionally sharing critical pieces of information with others. If this is the case, questions related to the value of most messages will arise concerning the value that unaffiliated participants bring to the network by only opining about preexisting news coverage.

Steinberg, Wukich, and Wu: Central Social Media Actors

Design and Methods

We examined the actors comprising Twitter hashtag networks during four extreme events and evaluated how information is shared. In an attempt to provide timely yet broad insight, cases were selected from within a five week time frame during April- May, 2013 and included extreme events which varied with respect to casualties and damage. Events include the terrorist attack and subsequent manhunt in and around Boston, Massachusetts; the fertilizer plant explosion in West, Texas; flooding in Peoria, Illinois; and the EF5 tornado that touched down in Moore, Oklahoma.4 Each of these incidents were federally declared disasters and received significant, albeit varied, traditional media attention; they provide diversity in terms of hazard type (terrorism, technical disaster, and natural disaster) and geographic region (Northeast, Midwest, and Southwest) and allow for the identification of an initial set of generalizable findings to guide future research (see Table 1). Using publically available Twitter data collected through NodeXL, we modeled several information networks and identified important actors. Using the “Import from Twitter Search Network Option,” we collected information networks facilitated by hashtags during four disasters. Hashtags were identified by monitoring the trending topics section in Twitter as the incidents occurred. Retweets (i.e., the direct copying and amplification of a message), mentions (identifying a user directly in an original message), and replying to another user’s message provide the basis for network relationships or ties. If a user retweets another’s tweet, then they are connected. If a user mentions another in a tweet, then they are connected; and so forth, as a user replies to another’s message. In the case of retweets, anyone retweeting a message presumably felt that the original content was important enough to disseminate; therefore users whose messages are commonly retweeted can be considered to be influential or important actors. We examined data from the onset of each incident through the subsequent seven days.5

Table 1: Cases by Type of Disaster, Geographic Region, and Onset Date

Type of Geographic Incident Disaster Region Onset Date Boston Marathon bombing Terrorism Northeast 15-Apr-13 West, Texas fertilizer plant explosion Technical Southwest 17-Apr-13 Peoria, Illinois flooding Natural Midwest 17-Apr-13 Moore, Oklahoma tornado Natural Southwest 20-May-13

Tweets examined are ones which employed specific hashtag categorizations identified by Twitter as a trending topic, one that is “immediately popular” and can

Steinberg, Wukich, and Wu: Central Social Media Actors

“help people discover the most breaking news from across the world” (Baweja 2010). Trending topics readily appeared on users’ dashboards and helped to garner widespread attention. For analysis, we chose trending topics that ranked highest and remained a trending topic for the longest time during the period under examination.6 In the case of Boston, we analyzed two different topics using the hashtags #bostonstrong and #bostonbombing which spanned consecutive days from April 15 to April 21. For the fertilizer plant explosion, we used hashtag #westexplosion and garnered data from consecutive days from April 17 to April 23. For the Peoria flooding, we used the hashtag #peoria on consecutive days from April 17 to April 23. For the Moore, Oklahoma tornado, we used the hashtag #prayforok on consecutive days from May 20 to May 26. The NodeXL software tool was used to gather and analyze all tweets with the identified hashtags found within this data set (Smith et al. 2010).7 The aggregate dataset contained 23,418 total tweets broken down as 9,160 for #bostonbombing, 6,231 for #bostonstrong, 5,656 for #westexplosion, 486 for #peoria, and 1,885 for #prayforok. Some users tweeted more than once; in terms of total users there were 4,408 users within #bostonboming; 5,817 users within #bostonstrong; 3,160 users within #westexplostion; 312 users within #peoria; and #1,778 users within #prayforok. Two attributes, user type and geographic location, were manually added to the dataset based on Twitter users’ self-descriptions. User types conformed generally to Vultee and Vultee’s (2011) typology and included government, nonprofit, media, business/commercial, and private users. Geographic locations were local, in-state, out of state, and international.8 Tweets were included in the sample if they contained the identified hashtag, and NodeXL’s importation tool created network data by linking Twitter users if a user retweeted another’s message.

Data Analysis

Social network analysis, specifically degree centrality statistics, provides a baseline assessment of the patterns of interaction that occurred between users. Using Node XL, we calculated degree centrality (the number of unique relationships for each actor) to identify who the key actors were. We defined a relationship as a retweet; if a person passed along a message, a relationship was created. The assumption was that the number of relationships and degree centrality offered a measure of an actor’s importance in the network. For our analysis, in-degree centrality represents the number of times a user was retweeted. If a user has a high in-degree value, this implies that the user’s messages are being retweeted by a large number of other users. Another way to think about this is that the Twitter community feels that the messages sent out by this user are important or interesting and are thus frequently shared. Out-degree represents the number of times a user retweeted another user’s message.9 A user with a high out-degree score would be promoting messages from external sources.

Steinberg, Wukich, and Wu: Central Social Media Actors

Findings

The Boston Bombings, April 15, 2013

The City of Boston was subject to a terrorist attack on April 15, 2013 when two improvised explosive devices were detonated close to the Boston Marathon finish line. The attacks led to multiple deaths and hundreds of injuries. This incident also led to a mobilization of emergency services responders who treated mass casualties and law enforcement personnel who both had to deal with the response and conduct of an extensive investigation and manhunt. As the national and local media broke the story, Twitter users were already sharing information about the event. The trending topics #bostonbombings and #bostonstrong were two of a handful of popular Boston-related topics. Other hashtags of note during the incident included #watertown, #boston, #bostonpolicescanner, and #oneboston. The hashtag #bostonbombings was the first to catch on among Twitter users, and the most popular hashtag used during the response. By using this hashtag, users categorized tweets which offered those looking for similar information a shortcut to find it. Table 2 lists the frequency distribution of Twitter users who participated in #bostonbombings by type and geographic location.

Table 2: Frequency Distribution of Twitter Users Participating in #bostonbombings by Type and Location

Local In-State Out-State International Total N % N % N % N % N % Government - - 1 0.02 5 0.11 - - 6 0.14 Media 28 0.64 10 0.23 321 7.28 72 1.63 431 9.78 Nonprofit 1 0.02 - - 34 0.77 5 0.11 40 0.91 Commercial 3 0.07 - - 49 1.11 6 0.14 58 1.32 Private Citizen 115 2.61 103 2.34 2747 62.32 908 20.60 3873 87.86 Total 147 3.33 114 2.59 3156 71.6 991 22.48 4408 100.00 Note: 1,958 private citizens who participated did not share their location on Twitter and are not included in this table.

Some Twitter users and messages were more popular than others. Table 3 lists the most prominent users by their in-degree centrality scores, indicating that the network was centered on media outlets. Eight of the top fifteen users were media sources as well as four of the top six users (each having an in-degree score of over 20): Adrienne Mong from NBC News; the official account used by the Christian Science Monitor; Glenn Greenwald (a journalist for the American online presence of the British print newspaper

Steinberg, Wukich, and Wu: Central Social Media Actors

The Guardian); and the official account used by MSNBC. Two prominent private citizens were David Icke (an English writer) and a user going by the handle polymath22 who self-identifies as “Social Media Enthusiast” and “Internet Troll.”10 The higher the in-degree value, the more time the user was retweeted. Therefore, the tweets by Adrienne Mong and David Icke were the most shared messages among the network. Polymath22 has the highest out-degree score, suggesting that this user was serving an important role of passing information on from others, as well as having their information passed on.

Table 3: Top Actors by InDegree Centrality #bostonbombings

Actor InDegree OutDegree Entity Location adriennemong 81 1 Media International davidicke 66 1 Private Citizen International csmonitor 45 1 Media Local ggreenwald 33 1 Media Out-state msnbc 23 1 Media Out-state polymath22 22 10 Private Citizen Local nlasmus 18 1 Private Citizen Out-state dabeard 17 1 Media Out-state susantran 17 1 Media Local glockster23 15 1 Private Citizen Out-state imfabulous13 15 1 Private Citizen Out-state wacade 15 1 Private Citizen Out-state 10newsnatasha 13 1 Media Out-state nbcnightlynews 13 2 Media Out-state norteildn 13 1 Private Citizen Out-state

With an in-degree score of 81, NBC News’ Adriene Mong proved to be the most retweeted participant. Her messages broke news specifically with reactions from the suspects’ family. Some of Mong’s more widely disseminated tweets include:

 Big crowd of journos waiting for #Bostonbombing suspects' mother to emerge from FSB building in #Dagestan capital. Been in there 6 hours.  #Bostonbombing suspects mother says, “I don't and I won't” accept sons were involved. http://t.co/0lOVSsFOmE  #Bostonbombing suspects’ dad may travel to US tomorrow from #Dagestan.

Steinberg, Wukich, and Wu: Central Social Media Actors

These tweets are emblematic of the #bostonbombing content which was largely made up of breaking news, followed by participant reaction. If a person wanted to search for new information about the suspects and response efforts, #bostonbombing was an appropriate venue. The other hashtag network under examination with respect to the Boston bombing, #bostonstrong, has a rather different network composition in terms of content description. This hashtag provided a platform through which to express goodwill and condolences to the City of Boston. Table 4 lists the frequency distribution of Twitter users who participated in #bostonstrong by type and geographic location. The vast majority, 92.48 percent, were private citizens and only a portion of them from the Boston area.

Table 4: Frequency Distribution of Twitter Users Participating in #bostonstrong by Type and Location

Local In-State Out-State International Total N % N % N % N % N % Government 11 0.25 1 0.02 2 0.05 1 0.02 15 0.35 Media 13 0.30 5 0.12 69 1.59 18 0.42 105 2.42 Nonprofit 15 0.35 1 0.02 16 0.37 4 0.09 36 0.83 Commercial 22 0.51 16 0.37 113 2.61 19 0.44 170 3.92 Private Citizen 538 12.41 223 5.14 2722 62.78 527 12.15 4010 92.48 Total 599 13.81 246 5.67 2922 67.39 569 13.12 4336 100 *1,481 private citizens who participated did not share their location on Twitter and are not included in this table.

The most active users employing this hashtag were not journalists; rather, they were celebrities (see Table 5). The top three users were actors: Kerry Washington (in-degree, 231), Eliza Dushku (in-degree, 128), and Eli Roth (in-degree, 108); Jim Lynch (in- degree, 100) and Sarah Colonna (in-degree, 73) were top writers. Kerry Washington’s tweet, “My thoughts and prayers are with Boston. Tweets are on hold as developments unfold. Stay strong #Boston. #BostonStrong,” was retweeted by 231 others and encapsulates the spirit and general message content of the network. Given the higher in-degree scores, it could be argued that the # bostonstrong network possessed a more effective architecture for sharing information than #bostonbombings. However, message content failed to address areas related to protective action decisions, the manhunt, or otherwise inform situational awareness. Instead, participating celebrities with high follower counts provided opportunities for others to express support to the city. Both Boston related hashtag networks show a correlation between follower counts and in-degree scores, since users with more followers were generally more likely to

Steinberg, Wukich, and Wu: Central Social Media Actors have their messages retweeted than other users, and in both networks, out-degree scores were low for influential users. Users whose tweets were re-tweeted were not often found to be retweeting others. This may be due to their focus on message creation rather than transmission of already existing messages.

Table 5: Top Actors by In Degree Centrality #bostonstrong

Actor InDegree OutDegree Entity Location kerrywashington 231 1 Private Citizen Out-State elizadushku 128 1 Private Citizen Out-State Eliroth 108 1 Private Citizen Out-State authorjimlynch 100 1 Private Citizen Out-State sarahcolonna 73 1 Private Citizen Out-State redostoneage 31 1 Private Citizen Out-State lizclarkebrown 27 1 Private Citizen Out-State Klizzzle 23 1 Private Citizen Out-State Mrjoezee 22 1 Private Citizen Out-State caffeinated_mom 10 1 Private Citizen Out-State clarionproject1 10 1 Nonprofit Out-State memichaeledward 10 1 Private Citizen Out-State thomaslemay 9 1 Private Citizen Out-State nbcwashington 8 1 Media Out-State erika_news 7 1 Media Out-State

Explosion in West, TX, April 17, 2013

Just two days after the bombings in Boston, on April 17, 2013, a fertilizer plant exploded in West, Texas, a town of about 2,800 people located just off of Interstate 35 north of Waco. The explosion killed fourteen people, twelve of whom were firefighter and paramedic first responders. Before regional and national media descended on scene, a video of the explosion shot by motorist Derrick Hurtt was posted on YouTube and quickly went viral.11 In this case, social media had beaten traditional media to the story. Like #bostonbomings and #bostonstrong, private citizens made up the majority of participants in #westexplosion, 76.85 percent (see Table 6). In this case, media entities comprised a significant portion of the network, 15.9 percent, and similar to #bostonbomings, played a central role in information dissemination. However, based

Steinberg, Wukich, and Wu: Central Social Media Actors on their out-degree centrality, media outlets were not just disseminating, but also searching for information and were open to pass on relevant messages (see Table 7).

Table 6: Frequency Distribution of Twitter Users Participating in #westexplosion by Type and Location

Local In-State Out-State International Total N % N % N % N % N % Government 1 0.03 9 0.31 3 0.1 0 - 13 0.45 Media 34 1.17 291 9.99 127 4.36 11 0.38 463 15.90 Nonprofit 8 0.27 54 1.85 30 1.03 4 0.14 96 3.30 Commercial 20 0.69 51 1.75 26 0.89 5 0.17 102 3.50 Private Citizen 79 2.71 941 32.31 1028 35.3 190 6.52 2238 76.85 Total 142 4.88 1346 46.22 1214 41.69 210 7.21 2912 100

Table 7: Top Actors by InDegree Centrality #westexplosion

Actor InDegree OutDegree Entity Location YourAnonNews 356 2 Media Out-state Jayfhicks 174 15 Media In-state NBCDFW 146 6 Media In-state Wacotrib 135 3 Media Local wfaachannel8 119 7 Media In-state Kwtx 91 2 Media Local DavidSchechter 85 2 Media In-state bynickdean 83 8 Media In-state KFCBarstool 78 1 Media Out-state latimes 65 1 Media Out-state YourAnonLive 65 1 Media Out-state startelegram 62 8 Media In-state TexasPrepares 61 1 Private Citizen In-state abc13houston 48 4 Media In-state OmarVillafranca 48 2 Media Local

The media-related entities had the highest in-degree scores, indicating that these sources were the most retweeted, as shown in Table 7. Most of the tweets by private

Steinberg, Wukich, and Wu: Central Social Media Actors citizens were retweets of media sources, implying that media-generated messages were considered to be important enough for other users to pass on. Jay F. Hicks, then director of news media for Waco’s CBS affiliate pushed out breaking news and was widely retweeted, and his messages helped to relay information about the cause of the explosion: “Fire Marshall says rail car full of ammonium nitrate was not the cause of #westexplosion.” Many of the central users represented traditional media outlets, although some represented new media such as @YourAnonNews. With a huge following (1,103,427 people) @YourAnonNews is the Twitter account associated with the media arm of the hacker activist group Anonymous. The account posted updates regarding the explosion which were often linked to other news stories. With an in- degree score of 356, @YourAnonNews far surpassed others in terms of their ability to disseminate information.

Flooding in Peoria, IL, #peoria

After a week of heavy rains in Peoria, Illinois in April 2013, the Illinois River crested at 14.35 feet above flood stage, displacing hundreds of families and shutting down key business districts (Peoria Journal Star 2013). Peoria was not alone, for serious flooding occurred simultaneously across many Midwestern communities. While many of these areas received federal disaster declarations, the incidences did not garner significant national media attention, especially compared to Boston and West. Twitter users, albeit in fewer numbers, still communicated about the situation using an established hashtag, #peoria. Table 8 demonstrates that private citizens made up the majority of participants (60.90 percent). The media participation was 20.42 percent and once again played the most central roles in information dissemination, as expected from hypothesis 2. Key actors included the city newspaper, Peoria Journal Star, and a handful of local television personalities (see Table 9). The Journal Star provided some of the only protective action information that we found among central actors across cases. For example, as the city flooded, the newspaper posted a list of road closings. Most messages, though, were news-oriented rather than protective action-oriented, as evidence by the two tweets below.

 Rising waters draw a crowd to #Peoria riverfront #peoriawx http://t.co/iFcTgjjHpU  Illinois River crests at 29.35 feet in #Peoria, breaking 1943 record #peoriawx http://t.co/MFqVZ16HFU

Private citizens, local residents among them, ranked as central actors as well. It can be inferred that their messages were considered to be important enough for others to pass on. However, as compared to previous cases, both the total participants and the in- degree scores are much lower, implying small retweet networks. As a side note, one of

Steinberg, Wukich, and Wu: Central Social Media Actors the governmental actors, Governor Pat Quinn, used #peoria to communicate that he was in the city assessing damage.

Table 8: Frequency Distribution of Twitter Users Participating in #peoria by Type and Location

Local In-State Out-State International Total N % N % N % N % N % Government 3 1.04 4 1.38 - - - - 7 2.42 Media 33 11.42 12 4.15 13 4.50 1 0.35 59 20.42 Nonprofit 6 2.08 2 0.69 6 2.08 - - 14 4.84 Commercial 10 3.46 9 3.11 11 3.81 3 1.04 33 11.42 Private Citizen 36 12.46 70 24.22 66 22.84 4 1.38 176 60.90 Total 88 30.45 97 33.56 96 33.22 8 2.77 289 100.00

Table 9: Top Actors by InDegree Centrality #peoria

Actor InDegree OutDegree Entity Location Pjstar 22 2 Media Local marcusbailey 14 1 Media Local danielle_hatch 5 1 Media Local andykravetz 5 1 Media Local Jdrch 4 1 Private Citizen Local jumpsimulation 4 1 Media Local sammywaller 3 1 Private Citizen In-state casper0311 3 1 Private Citizen In-state jimkrasulacbs 3 1 Media Out-state dennisedit 3 3 Media Local GovernorQuinn 3 1 Government In-state christianpalos 2 1 Private Citizen Out-state stechk 2 2 Private Citizen Out-state mohickman18 2 1 Private Citizen In-state tchiderov 2 1 Private Citizen In-state

Steinberg, Wukich, and Wu: Central Social Media Actors

Tornado in Moore, OK - #prayforok

Only a month after the Boston, West, and Peoria disasters, an ESF 5 tornado decimated much of Moore, Oklahoma, a suburb of Oklahoma City, killing 23 people and injuring hundreds more. The immediate response and recovery efforts received significant national media attention and many turned to Twitter to express condolences. Although hashtags such as #moore and #tornado were used, the trending topic was #prayforok. The “prayfor” phrase is one applied to other situations and generates a large number of faith-inspired sentiments. As demonstrated in Table 10, private citizens made up almost the entire set of participants (96.75 percent). Government entities played no role, and the roles of nonprofits and media proved to be negligible, representing 1.11 percent and 1.37 percent of participants respectively. Individuals were also the major players when looking at in-degree scores.

Table 10: Frequency Distribution of Twitter Users Participating in #prayforok by Type and Location

Local In-State Out-State International Total N % N % N % N % N % Government 0 - 0 - 0 - 0 - 0 - Media 1 0.09 1 0.09 13 1.11 1 0.09 16 1.37 Nonprofit 0 - 1 0.09 12 1.03 0 - 13 1.11 Commercial 0 - 0 - 9 0.77 0 - 9 0.77 Private Citizen 35 2.99 198 16.94 863 73.82 35 2.99 1131 96.75 Total 36 3.08 200 17.11 897 76.73 36 3.08 1169 100

Despite the much larger network size as compared to Peoria, IL, the in-degree scores are still relatively low, implying a small retweet network (see Table 11). Content focused on expressing condolences and well wishes. The most retweeted message implored those not adhering to informal norms to be more respectful; Twitter user @1_alexandriaa_3 tweeted “All jokes aside now, there have been fatalities...” Despite opportunities for faith-based organizations involved in the response, no explicit fundraising or other resource requests were made by organizations, although a handful of less influential individuals promoted fundraising. Future users of similar hashtags may want to operationalize the network in that way.

Steinberg, Wukich, and Wu: Central Social Media Actors

Table 11: Top Actors by InDegree Centrality #prayforok

Actor In-Degree Out-Degree Entity Location 1_alexandriaa_3 9 1 Private Citizen In-state jharrisfootball 8 1 Private Citizen Out-state teresagroff16 6 1 Private Citizen Local marcylauren 6 1 Private Citizen Out-state joethepatriotic 6 1 Private Citizen Out-state ashliedubuy 6 1 Private Citizen Out-state raygensmith 4 1 Private Citizen Out-state tannerberryhill 4 1 Private Citizen Out-state q_oconnor 4 1 Private Citizen Out-state cbcjonesboro 4 1 Private Citizen Out-state candyshop08 4 1 Private Citizen Out-state mcbridealex20 4 1 Private Citizen In-state tgun13_ 4 1 Private Citizen Out-state alison_wells 4 1 Private Citizen In-state auntrayray 3 1 Private Citizen Out-state

Overall Trends and Types of Information Networks Identified

We expected that individual users, as opposed to organizations, would constitute the majority of these information networks and that media actors would occupy key roles (see hypotheses 1 and 2). Our findings confirm those expectations. Private citizens overwhelmingly comprised the hashtag networks #bostonstrong, #prayforok, and to #peoria, to a lesser extent. The high percentage of private citizens provides opportunities, potentially, for agencies charged with response to disseminate information as well as to monitor messages for situational awareness. However, most participants resided outside of the affected areas, and the extent to which government and nonprofit agencies participated in these networks appears to be rather limited. In seeking to identify who the important actors were within disaster related hashtag networks, various types of networks were identified, although this was not part of the aim or scope of the initial project. Within these networks were both media-centered and individual user-centered networks (See Table 12). In the cases of #bostonstrong, #prayforok and, to a lesser extent, #peoria, private citizens dominated in terms of the most retweeted messages. In this way, these networks can be described as citizen-centric with influential citizens playing important roles. In the case of #bostonstrong, central users were celebrities; #bostonstrong could be

Steinberg, Wukich, and Wu: Central Social Media Actors considered to be celebrity-centered since key actors within this network were not average citizens but were rather famous with a much larger reach than a typical Twitter user in regard to both their personal notoriety and their number of followers.

Table 12: Network Typologies and Potential Value to Decision Making

Potential Potential value to value to those those Type Example affected responding Government & N/A High High nonprofit-centered #westexplosion, Media-centered Medium Medium #bostonbombings Media-centered, #peoria High High Location-based

Celebrity-centered #bostonstrong Low Low

Citizen-centered, #peoria High High Location-based Citizen-centered, #prayforok Medium Low Issue-based

Participants in #prayforok were private citizens from outside of Oklahoma who used the forum as an opportunity to express faith-based condolences. In terms of #peoria, participants were largely local residents who communicated their flood experience; many of these were messages that were retweeted by other members of the community. While both #peoria and #prayforok had significant private citizen participation, #peoria could be described as a hybrid network given the importance of some media, notably local media, within the network. While #peoria was location- based-those participating in the hashtag network were mostly local-#prayforok participants represented a diversity of location and focus on their faith-based messages. The hashtag networks #bostonbomings and #westexplosion coalesced around media actors, both traditional outlets and citizen journalists. In that way, those networks appear to be media-centered. Journalists who use Twitter on a regular basis and develop significant followings have the capacity to make major impacts on the flow of information. In Boston, a terror attack on the East Coast deservedly received considerable attention from national media outlets. In West, regional media outlets played more of a role in disseminating information. The different network types described imply varying levels of potential in terms of information value. One network type not demonstrated by our cases, government and nonprofit-centered, ideally would generate useful information sources to both households affected and the array of agencies engaged in disaster response (see Hughes 65

Steinberg, Wukich, and Wu: Central Social Media Actors et al. 2014; Sutton et al. 2014; Wukich 2015; Wukich and Mergel 2015). The lack of highly connected government and nonprofit agencies, at least in the context of these information networks, suggests a disconnect between officials and those affected, a disconnect that can lead to information asymmetry and suboptimal disaster management results when the government does not take full advantage of the technology and resources available to them. Media-centered networks potentially act as conveyors for government messages and independent reporting to those affected. However, in the case of #bostonbombing, messages by central actors aim to satiate the national audience’s desire for updates on the investigation. We can imagine media-centered networks with a local focus that better inform those affected regarding protective action decisions and/or government- initiated crowd sourcing requests. In fact, we propose that locally based networks, whether citizen or media-centered, may be more focused on providing valuable information to those affected than other types of networks. In particular, celebrity- centered networks appear to provide little information of value other than encouragement. Celebrity centered and issue-based networks, though, have the potential to raise awareness and fundraise for those affected, although we found little evidence of that happening in the cases examined. Conforming to our expectations (see hypothesis 3) and in line with the typologies presented, network participants were not limited to local jurisdictions. Instead, the vast majority of users in all cases came from outside of the affected area with local participants making up relatively limited shares of the total number of participants. Local users made up a significant portion of the network only in the case of #peoria. However, even in this case, only 30.45% of the tweets came from local users, as compared with less than 15% in other cases.

Discussion and Conclusion

We found that even though many organizations involved in disaster response are using social media (Sutton 2010; Sutton et al. 2013; Vultee and Vultee 2011), these agencies still play a limited role within hashtag communities (Wukich and Steinberg 2013). In their absence, media and private citizens constitute a greater percentage of information networks (Hughes and Palen 2009; Sutton et al. 2008) and also assume key positions as information hubs, which reaffirms previous research. The utility of the information that these key actors disseminate, however, is questionable. The media matters. The results related to #bostonbombings and #westexplosion show that media actors demonstrate top centrality scores–they are key information hubs. These results are consistent with the PADM model (Lindell and Perry 2012) and Hermida’s (2010) research. In addition, the results of our study suggest that central actors are not limited to local jurisdictions; findings are consistent with past research (see Heverin and Zach 2012; Sutton, 2010). Nonlocal participants serve as Twitter central actors across incidents. The utility of their messages, though, deserve more evaluation to determine if these central actors provide value to affected residents and

Steinberg, Wukich, and Wu: Central Social Media Actors emergency managers scanning social media for situational awareness, or whether they are just taking part in a conversation. We evaluated tweets from the most central actors and noted only a limited number of messages related to protective action decision making or tweets that were valuable to building situational awareness. Local media did pass along alert and warning information, but these important messages could easily be lost among the large amount of information. We would expect government or nonprofit-centered networks to demonstrate more utility with respect to protective action decision making and the development of situational awareness, but none of the networks analyzed in this paper fit that description. Overall, we see a trend in which traditional and new media outlets are providing the public information through Twitter hashtag networks. But there are also other networks where celebrities and other private citizens tend to be central actors. Within certain networks, such as the media-centered or location-based networks, information seems to have a higher degree of utility than within celebrity and other citizen-centered networks; within these networks, less central figures may be passing along relevant and valuable information. While individual messages from less influential actors may provide utility, we chose to focus on the message content of key actors based on the extent to which their messages permeate throughout the network. Our initial assumption was that those messages that are retweeted the most by the public are deemed to be the most useful; however, we found this not to be the case in every network. Most retweets were related not to protective action decisions, but instead to observations and commentary. Further analysis is warranted to determine the degree of utility of message content and the value of individual messages to the public during these incidents. This could be a useful next step to better assess overall network utility and reach. The composition of the networks studied and key actors identified helped us to develop a typology of disaster hashtag networks. Being able to identify typologies allows for the meta-analysis and perhaps will allow for the development of standards in hashtag use during an emergency. Through further research, we may be able to identify the type of communication within a network based on the signaling characteristics of the hashtag. As people increasingly seek out disaster related information from social media, it is important to understand from where they derive this information. The Boston incident indicates that for large-scale events in which there are multiple hashtags in use, each can signal a different information stream, much like the different television or radio stations that provide their own unique brand or style of information. However, with smaller events, there may only be one hashtag that is adopted and used. A limitation of our study was that we did not analyze every hashtag network for each incident. Instead, we focused on the most popular, with the exception of Boston, where we examined the two most popular hashtags. Our emphasis was to make comparisons of trending topics across cases, rather than to evaluate each incident’s hashtag universe. Future research could account for all hashtags used in a particular disaster to better understand the range of channels created. In addition, the use of 67

Steinberg, Wukich, and Wu: Central Social Media Actors hashtags limits analysis to established events as opposed to being able to examine emerging events. Further research may wish to use more generalizable search terms rather than hashtags to look into this phenomenon. We found evidence of a range of channels. In the networks we examined, different message types appeared, with different intended audiences. For the Boston incident, two separate hashtags allowed for a separation of this information along channels that a user could select from, similar to how they may choose to listen to talk radio versus national public radio for political news. Fewer hashtags were adopted for the other incidents, with smaller networks consisting of fewer users and tweets, leading to communication streams dominated by different users depending on the incident. Future studies can work to pinpoint the most useful messages and how others might employ this information in order to identify best practices both in regard to tweet substance as well as network engagement. In addition, how individuals use information for actual protective action decisions could be gauged, instead of measuring usefulness by additional twitter activity. Those affected by disaster may not retweet information, but rather use it to improve their positions. As more people adopt Twitter and other social media tools, it is not clear if hashtags will continue to provide a means to separate the signal from the noise, or if the information is always useful to everyone within a hashtag network. But for the time being, hashtag networks are becoming an increasingly used source of disaster related information and thus are worthy of continued study.

Acknowledgements

The authors thank Franklin Leung, Derek Mayrant, and David Pratt for their assistance with data coding, Andrew Williamson for his research support, and Johnny Nguyen for his technical guidance. An earlier version of this paper was presented at the Southeastern Conference of Public Administration (SECoPA), in Charlotte, North Carolina on September 27, 2013. This research was supported by Sam Houston State University’s College of Humanities and Social Sciences, the Department of Political Science, and the Center for the Study of Disasters and Emergency Management.

Notes

1 Facebook recently started using the hashtag modifier for similar purposes, but the tool is not as developed or as well used in this online social network. 2 Following, Favoriting, and Mentioning are other means of creating a network via Twitter, but are not examined within this paper. We feel that the Following metric is often an over-inflated means for understanding the importance of an individual as higher follower counts does not necessarily mean more people are actually seeing a particular message. Favoriting was considered for a metric of gauging the importance

Steinberg, Wukich, and Wu: Central Social Media Actors of a message to other individuals, but since we are focused on communication sharing and that this option does not lead to a message being proliferated, it was not used in this study. Mentioning is a means to get another Twitter user to notice a post or to acknowledge a previous post by another user. It use was rare within this data set and the metric itself is not a useful way to measure information sharing or dissemination. 3 Twitter users’ self-identification was the initial source of classification. The use of an identified commercial media outlet as part of the self-identification led to the label of media. For example, both the official account of NBC and any account by a reporter working for NBC would be classified as media. In addition, any self-identification as media or news was classified as media. For example, if someone self-identified as a Blogger and included an established blog/website, unless the Twitter feed looked particularly unprofessional or questionable, the node was labeled as a private citizen. When there was no self-identification as media or if the self-identification specifically said something to the effect of “the views here are my own,” the node was labeled as a private citizen. While it is possible for someone to falsely self-identify, for example, saying they are a reporter for a media outlet, we accepted the self-identification unless the Twitter feed looked particularly unprofessional or questionable, in which case the node was labeled as a private citizen. 4 The Enhanced Fujita (EF) scale is used to measure wind strength. EF 5 is the highest classification, indicating wind speeds above 200 mph. 5 We identify any user who employed a hashtag under consideration within his or her tweet as a network member. The dataset includes original messages as well as retweets. Users created network ties, or relationships, by retweeting, replying to, mentioning other messages. This operationalization of network ties offers a broad set of possible connections that facilitated information exchange. 6 The Midwest flooding as a topic did not receive a trending status designation. The hashtag #peoria was chosen due to its high Twitter search results rank based on the terms Midwest and flooding. As #peoria proved to be a regularly used hashtag category, messages that did not have to do with flooding were removed. 7 Some of the captured data, while technically part of the hashtag network, was never retweeted by anyone. In NodeXL, a user with an in-degree of 1 and an out-degree of 1 created content, but that content was never re-tweeted. While followers of that user potentially gained value from the information, the information was not deemed valuable enough to pass on. This is likely to happen when the user is a business or private user with few followers such as a small personal network. 8 Location was derived from users’ volunteered data. Coders were unable to ascertain user location in many cases, predominantly those of private citizens. 9 In this network, the out-degree metric counts the number of other users the user in question retweeted. If user A retweeted user B, user A’s out-degree score would be 1. If the user only has self-generated content in the network, i.e. does not retweet anyone, the value of the out-degree metric would still be the minimum of 1, we identify this action as a self-loop. Retweets were captured based on the use of the retweet button within Twitter.

Steinberg, Wukich, and Wu: Central Social Media Actors

10 Polymath22’s account has since been suspended, limiting the ability to figure out more about this user. 11 Explosion in West, Texas - Derrick Hurtt video. Retrieved April 18, 2013.

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