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Risk, Hazards & Crisis in Public Policy, Vol. 4, No. 2, 2014

Nonprofit and Public Sector Participation in Self-Organizing Information Networks: Twitter Hashtag and Trending Topic Use During Disasters Clayton Wukich and Alan Steinberg Twitter’s hashtag categorizations and trending topics offer accessible tools through which people communicate and self-organize during disasters. This article examines Twitter communication networks, specifically the roles played by nonprofit and government agencies, that were engaged during four disasters: (1) The Boston Marathon bombing, (2) The West, Texas fertilizer plant explosion, (3) The Midwest spring flooding in Peoria, Illinois, and (4) The Moore, Oklahoma tornado. NodeXL and UCINET were used to collect and analyze data and determine network composition and structure. Nonprofit and government Twitter pages provided additional data. Findings indicate varied levels of engagement. While several state-level public agencies and regional nonprofits used Twitter with some regularity, many failed to participate in hashtag networks that penetrated disparate user groups. The article concludes with a discussion on how organizations might more effectively promote response efforts through this medium. KEY WORDS: disaster management, social network analysis, Twitter, and nonprofits

Introduction By definition, disasters disrupt normal routines (Drabek, 2010; Perry & Quarantelli, 2005), and these disruptions require coordinated effort across social sectors and levels of jurisdiction to protect life and property and restore continuity of operations. However, information asymmetries impose barriers to effective coordination since silos and bottlenecks deny useful information to those who need it. Less constrained communication, therefore, is important. Developments in information technology have promoted information sharing and reciprocity across fields of practice (Comfort, Ko, & Zagorecki, 2004; Fountain, 2001; Kapucu, 2006). The use of social media for emergency management represents one example. Social media provide mechanisms to bridge boundaries, allowing for the scale-free dissemination of critical information. The sheer amount of information online can be overwhelming, making it difficult to separate the signal from the

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noise (Kavanaugh et al., 2012, p. 481). Social networking tools, especially Twitter with its self-organizing hashtag categorization communities, may help with this problem. This article examines how the use of hashtags facilitates the dissemination of information during extreme events, focusing on the role of nonprofit and government agencies that have traditionally organized response efforts. The composition and structure of Twitter hashtag networks during four incidents are examined and nonprofit and government agency involvement is evaluated. While nonprofits, particularly regional chapters of the American Red Cross, were active Twitter users, government agencies at all levels missed opportunities to inform the public and promote information exchange via social media. Additionally, this article shows that there is a lack of coordination between government and nonprofit agencies regarding the selection and use of disaster-related hashtags. Social Media Social media “refers to Internet-based applications that enable people to communicate and share resources and information… examples include blogs, discussion forums, chat rooms, wikis, YouTube Channels, LinkedIn, Facebook, and Twitter” (Lindsay, 2011, p. 1). During disasters, these sources of information offer decision support for emergency managers and effected citizens alike because risk can be detected, interpreted, and communicated by multiple actors and broadcasted in real-time to create more robust common operating pictures. Nonprofit and government agencies, consistent with their missions, can use these tools to monitor messages, disseminate public information, or directly communicate with citizens (Crowe, 2012; White, 2013). People increasingly use social media through a variety of platforms (e.g., cellphones and tablets) to access and disseminate information during disasters (Hughes & Palen, 2009; Sutton, 2010). According to a 2012 poll commissioned by the American Red Cross, social media users were more likely to seek out and exchange information, in general, than others (American Red Cross, 2012a). In the event of a disaster, 76 percent of all respondents indicated that they would contact friends to see if they were safe via social media and 25 percent would download an emergency application to seek out additional information. Twelve percent of the population claim to have already used social media in the past to access or share information during an emergency. Nonprofits and public agencies, therefore, have an opportunity to engage a growing segment of the public through various social media tools. Self-Organization During Disasters As disasters disrupt continuity of operations, government and nonprofit organizations develop new norms and strategies in order to adjust (Drabek & McEntire, 2002). These actors rely on external information and other resources to develop situational awareness and facilitate operations; therefore multi-actor

Wukich/Steinberg: Self-Organizing Information Networks

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interaction is required and creates the basis for self-organizing response systems. These types of multi-actor networks represent complex adaptive systems in which people generate and respond to signals and boundaries that make action possible (Holland, 2012). Here, a variety of strategies can be tested, communicated, and adopted (or rejected) through a process of multilateral interaction and selection (Axelrod & Cohen, 1999). In this way, actors self-organize in response to specific stimuli. According to Comfort (1999), self-organization during disaster is the process through which a more systemic order arises out of local interactions intended to reduce shared risk. The researcher notes: Self-organization depends upon a viable set of communication and information processes that enable the participants in a system to make informed choices. Self-organizing systems spontaneously reallocate resources and rearrange their activities to create a better “fit” between their internal operations and their immediate external environment (1999, p. 40). Multi-way communication networks are critical to the process. During emergencies and disasters, uncertainty manifests as events create novel and complex problems (Flin, 1996; Klein, 1998). Communication breakdowns and other barriers create information asymmetries in which actors either lack relevant information or adequate channels to transmit feedback (Comfort, 2007a). Whether formally designed or improvised, the patterns of interaction that occur create a social structure. Networks, in general, do not form randomly; instead, they often are centered on key hubs that disproportionately demonstrate larger numbers of connections (Baraba´si, 2009; Baraba´si & Albert, 1999). Structural position, where an actor is in relation to others, impacts the flow of information and other resources (Burt, 2005; Lazer & Friedman, 2007). In emergency management, the individuals and the organizations connected to powerful and/ or resourceful actors have greater access and therefore a greater likelihood of successful performance (Comfort & Haase, 2006). Core-periphery structures may develop in which some organizations have greater access than others (Robinson, Eller, Gall, & Gerber, 2013). Organizations may take primary, secondary, and tertiary roles within these structures (Brudney & Gazley, 2009) with some adopting strategies for network brokerage (e.g., connecting to disparate groups) and others adopting strategies aimed at closure (e.g., interacting with similar agencies) (Wukich & Robinson, 2013).1 Problems related to inadequate information and resource flow during extreme events have been widely acknowledged (Comfort, Boin, & Demchak, 2010; U.S. Congress, 2006). Practitioners have spent considerable effort in developing policies that facilitate interorganizational communication, including the Incident Command System and the National Incident Management System (Moynihan, 2009). However, implementation has been uneven across levels of government (Jensen, 2009) and information asymmetries still emerge because different organizations, communities, and social groups that operate outside of these

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frameworks are denied critical pieces of information that could affect performance (Aldrich, 2012; Comfort, 2007a). Information technology provides the means to more readily span organizational boundaries (Fountain, 2001) and facilitates self-organization through decision support during both extreme events (Comfort et al., 2004) and preparedness activities (Mendonça & Fiedrich, 2006; Reddick, 2011). This research on information technology use, however, has focused on interorganizational communication and has not explicitly incorporated the participation of government and nonprofit agencies in open access networks that contain the public at large. Emerging internet communication technologies provide the capability to rapidly scale multiple levels of action (e.g., individuals, households, organizations, and multi-actor networks) and reduce the number of existing information silos. Social Media’s Role in Self-Organization During Disasters Social media provides just such an opportunity for self-organization. Users of the social media tool Twitter make use of hashtag categorizations to form ad hoc groupings of messages related to specific events or themes on a daily basis. Hashtags are a community-driven convention for adding context via metadata to a tweet and provide a means to create “groupings” of information (Leaman, 2009). The use of hashtags, retweeting messages, and/or following individual users helps to create a larger information network (White, 2013). Hashtags offer a public and accessible platform through which multiple actors, who might have no previous connection, can share and respond to information. Twitter’s trending topics identify popular hashtags to users via their individual dashboards, thus further amplifying trending categories. The use of hashtags came to Twitter in August of 2007 when Chris Messina sent out a tweet suggesting that the pound symbol be used for organizing groups on Twitter (Parker, 2011). Coincidentally, one of the early uses came during the 2007 wildfires in southern California that October. Nate Ritter’s use of the hashtag #sandiegofire allowed for the formation of a depository of help requests and a compilation of warnings and other information relevant to situational awareness (Messina, 2007; NewSquared, 2008). Hashtags vary in popularity, duration, and content. In general, users employ hashtags to classify their individual comments. Many hashtag topics are a simply a collection of opinions. Bruns and Stieglitz (2012), however, find that during disasters, users are more likely to retweet messages from other sources, suggesting the behavior could be due to the users’ desire to share critical pieces of information with others. Paying attention to and sharing the messages of others help to create a more interconnected network. Bruns and Burgess (2012, p. 6) note: A high volume of such response messages would indicate that users are not merely tweeting into the hashtag stream, but are also following what


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others are posting; the more such messages are contained in the hashtag stream, then—and the greater the total number of participants who engage in this way—the more can the hashtag community be said to act as a community. Larger, self-organizing information networks provide opportunities for nonprofit and government agencies to communicate with the public. If users pay attention, pieces of critical information may influence behavior and promote resilient communities. Nonprofits and Government Use of Social Media As social media tools gain popularity, nonprofit and government agencies adopt various strategies and tactics to engage users. Nonprofits appear to be innovating. In 2012, for example, the American Red Cross partnered with Dell Inc. to institute a “Digital Operations Center” which adapts corporate best practices in terms of social media data monitoring and advertising to disaster response (Clolery, 2012). Wendy Harman of the Red Cross noted that “In a world where people can organize themselves without needing institutions or infrastructure, we have to adapt to provide twenty-first century humanitarian services” (as quoted in Clolery, 2012). The Red Cross both disseminates information to the public and receives it. Mobile apps, for example, provide information on first aid, finding a shelter, and risks endemic to specific hazards like tornadoes, hurricanes, earthquakes, and wildfires (American Red Cross, 2012b). The Red Cross also monitors multiple social media platforms in order to increase their situational awareness, including general evaluations regarding the impact of an incident as well as direct interaction with individuals who contact the organization for assistance. The situational awareness that Red Cross managers accrue informs operational decisions such as the deployment of resources and where to concentrate efforts. During Superstorm Sandy, “the Red Cross pulled more than two million posts for review. … Thirty-one digital volunteers responded to 2,386 of the reviewed posts… 229 posts were sent to mass care teams, and 88 resulted in a change in action on ground operations” (Estes Cohen, 2013). Data mining social media during a disaster is not limited to nonprofits. A growing number of government officials use social media to monitor incidents and receive direct constituent feedback (Lindsay, 2011; White, 2013). The Mayor of Newark, Corey Booker, for example, has received widespread media recognition for his use of social media to identify problem areas and has personally responded at times with his snow shovel in hand (Dailey, 2011). At the state and federal levels, officials increasingly acknowledge the utility of social media and continue to develop and implement best practices (Sykes & Travis, 2012). The more intuitive value from this technology, however, is the mass dissemination of critical information. Through geographically targeted text messaging (SMS), FEMA established an emergency warning and alert system that

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micro targets citizens at risk in 2011. Social networking tools like Twitter allow for another means of communication that may be more robust than cellular phone service alone during a disaster. Considering the extent to which organizations are adjusting their communication strategies and incorporating internet communication technologies, systemic evaluations of the impact of these policies deserve attention. While some government and non-profit agencies are making use of social media, the degree of adoption and use by these agencies is not well known. Moreover, even less is known about the role of government and nonprofits in the formation and maintenance of ad hoc information networks that emerge during disasters. In fact, the extent to which these interactions aggregate to form information networks has received scant attention. White (2013) acknowledges the possibility of creating information networks by following users, retweeting messages, and using hashtags. Vultee and Vultee (2011) anecdotally identify that some users, most notably traditional media outlets, have their messages retweeted; thereby amplifying their content in pushing information out to otherwise disconnected users. Nonprofit and government agencies participate in these social media networks to some extent (Sutton, 2010), but there has yet to be an examination of the resulting network structures and processes across cases. This article examines four cases and makes propositions regarding how emergency managers and other officials across social sectors and levels of jurisdiction use or fail to use Twitter to engage a broader set of actors. Based on past research and evidence from practice, the article investigates the following hypotheses: Hypothesis 1: The government and nonprofit actors in these networks will not be limited to local jurisdictions. During large-scale disasters that receive national media attention and federal declarations of disaster, it is expected that government and nonprofit agency participation will not be restricted to local actors. Given that social media allows for large scale participation, it is expected that organizations outside of the disaster would be better able to participate in these information networks and will do so. Additionally, previous studies have found the users of hashtag networks tend to be unaffiliated individuals (Heverin & Zach, 2010). So while it is expected that local agencies will still participate, there will also be participation from state, local and possibly international agencies. Additionally, state and national actors may be more likely to participate given their greater degree of resources. It is assumed that organizations with more resources and personnel, as well as the perception that a significant number of their constituents use social media, will be more likely to use social media. For example, the Federal Emergency Management Agency (FEMA) will have a much larger social media presence than most local emergency management agencies. Additionally, national level organizations will have more experience with social media use and are more likely to identify hashtag categorizations than smaller


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jurisdictions. It is expected that local agencies both public and nonprofit are less likely to devote personnel and resources to disseminate information via social media during an incident. However, local organizations that represent large populations are more likely to have resources available as well as to be social media savvy. Therefore, they are expected to have larger levels of participation than smaller local actors and are more likely to identify relevant hashtags in order to communicate their message to a wider audience. Hypothesis 2: The government and nonprofit actors in these networks will play an important role in that they will be central actors within Twitter networks during disasters. Given that government plays a central role in disaster information sharing, it is expected that they will continue to be information hubs in social media networks. Network centrality scores provide measures. FEMA has previously made use of high-tech solutions to get messages to the public during times of extreme events. Based on the Red Cross’s use and promotion of using technology during extreme events, it is expected that critical pieces of information will be injected into the information networks by nonprofit and government agencies, just as these agencies provide critical information to traditional media sources. Hypothesis 3: Government and nonprofit actors will play a limited role within hashtag networks during disasters. Given that many agencies have only recently adopted the use of social media, if they have at all, it is not expected that many government and nonprofit agencies will be aware and make use of best practice techniques, such as participating in hashtag networks. Given the assumptions from the first hypothesis, it is also expected that local agencies with less resources will play a more limited role than that of state or national level agencies. Government and nonprofit agencies often lack high numbers of followers as compared to media agencies and even some private citizens, and this lack of followers and the subsequent lack of potential retweeters will limit the information dissemination capabilities of government and nonprofit agencies. Design and Methods In order to address the hypotheses, this article uses a dual research design. The first part of the study focuses on the composition of specifically identified hashtag based response networks by examining the government and nonprofit actors involved and tracking how information is passed. The second part of the study identifies the major government and nonprofit agencies that would be expected to play a role during the identified events and examines their Twitter activities in order to identify their baseline level of social media activity. In order to do this, our study employs a mixed-methods, multiple case study approach to investigate and extend theory.

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Case Selection Four extreme events that occurred in April and May of 2013 offer opportunities to examine the phenomena; they are (1) The terrorist attack and subsequent manhunt in and around Boston, MA, (2) The fertilizer plant explosion in West, TX, (3) The flooding in Peoria, IL, and (4) The EF5 tornado that touched down in Moore, OK.2 Each incident received federal disaster declarations as well as significant, albeit varied, media attention. These incidents provide diversity in terms of hazard type (terrorism, technical disaster, and natural disaster) and geographic region (northeast, midwest, and southwest), allowing for the identification of an initial set of generalizable findings to guide future research (see Table 1). The events represent roughly concurrent incidents that allow for fair comparisons of social media usage across geographic lines and among levels of agencies. Concurrence implies an equal potential for exposure to social media technology and prevents the lessons learned from one incident being used to influence subsequent incidents. Data and Methods This article uses publicly available data from Twitter contributors in order to model the information network and examine agency activity more generally. Tweets (e.g., short microblog messages posted on Twitter) were examined within a specified date range (the onset of an incident plus 6 days). The first part of the analysis focuses on tweets that employed specific hashtag categorizations identified by Twitter as a trending topic—one that is “immediately popular” rather than topics that have been popular for a while which “help people discover the most breaking news from across the world” (Baweja, 2010). Trending topics readily appeared on the dashboard of Twitter users and thus helped to garner widespread attention. For each incident, with the exception of the Peoria flooding, the authors selected a trending topic for analysis that ranked highest and remained a trending topic for multiple days. The Midwest flooding as a topic did not receive a trending status designation, however. The hashtag #peoria was chosen due to its high Twitter search results rank based on the terms Midwest and flooding.3 While other posts may be interesting and relevant, without the use of the hashtag users would not know where to look to find that information, and thus it

Table 1. Cases by Type of Disaster, Geographic Region, and Onset Date Incident Boston Marathon bombing West, Texas fertilizer plant explosion Peoria, Illinois flooding Moore, Oklahoma tornado

Type of Disaster

Geographic Region

Onset Date

Terrorism Technical Natural Natural

Northeast Southwest Midwest Southwest

April 15, 2013 April 17, 2013 April 17, 2013 May 20, 2013


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is not part of the information networks under examination here. The Tweets were collected using NodeXL’s importation tool.4 Twitter activity regarding the Boston Marathon bombing was captured using the hashtag #bostonstrong on consecutive days from April 15 to April 21. Twitter activity regarding the West, Texas fertilizer plant explosion was captured using the hashtag #westexplosion on consecutive days from April 17 to April 23. Twitter activity regarding the Peoria flooding was captured using the hashtag #peoria on consecutive days from April 17 to April 23. Twitter activity regarding the Moore, Oklahoma tornado was captured using the hashtag #prayforok on consecutive days from May 20 to May 26. The aggregate data set contained 14,258 total tweets broken down as 6,231 for Boston, 5,656 for West, 486 for Peoria, and 1,885 for Moore. A total of 11,067 users participated (5,817 for Boston; 3,160 for West; 312 for Peoria; and 1,778 for Moore). Two attributes, user type and geographic location, were manually added to the dataset based on Twitter users’ self-descriptions. User types confirm generally to Vultee and Vultee’s (2011) typology and include government, nonprofit, media, business/commercial, and private users. Geographic locations include local, in-state, out of state, and international.5 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. The second part of the analysis identifies and examines the Twitter presence of government and nonprofit agencies that operated in the affected areas, since many agencies failed to use hashtags and many simply did not use Twitter. Nonprofit and government agencies were identified for each event and by level of jurisdiction (national, state, regional, and local).6 At the Federal level, the study focuses on President Obama and FEMA’s Twitter feeds as well as the national feeds for the Salvation Army and the United Way. At state and local levels, different agencies made use of Twitter in each of the states. Attempts were made to find similar users when possible. This includes the governor in three of the four states, state level emergency management or public safety offices in three of the four states, and a general state government account in two of the four states. At the state level, the focus was on the Red Cross, Salvation Army, and the United Way as nonprofit agencies. This included the Central Illinois Red Cross and the United Way of Chicago for the Peoria, IL event; the Heart of Texas Red Cross and the United Way of Texas for the West, TX event; the United Way of Massachusetts for the Boston, MA event; and the Red Cross, Salvation Army, and United Way of Oklahoma for the Moore, OK event. On the local level, comparable agencies were even more difficult to identify because Boston, on one extreme, had separate accounts for city emergency services, fire, police, the mayor and the city council while the West, TX event, on the other extreme, only had a local level emergency management office represented on Twitter. Local nonprofit agencies were similarly available only sporadically; Peoria, Boston, and Moore had a local

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chamber of commerce using Twitter; Peoria had a local Salvation Army agency using Twitter; and Boston’s local United Way used Twitter.7 Collected Twitter data provide a set of baseline measures to gauge activity. Every tweet made by the identified organizations during the corresponding previously identified time periods was gathered.8 To examine the extent to which key actors were represented in these networks and the extent of their Twitter activity, the tweets were analyzed and coded to determine if they related to the event in question, which hashtags were used, which were retweets from other users, and which tweets by the organizations were retweeted by other users.9 In an ideal situation, there should be a high level of relevant tweets in the days immediately following the event in order to provide clear messaging to the public, but interest in even extreme events wans over time. However, it would be useful for the public to know which sources would provide the strongest signal to noise ratio so they may know which agencies to turn to during a disaster to get the most relevant information. Data Analysis Network analysis makes it possible to examine network structure and the limited involvement of nonprofit and government agencies. Content analysis of the tweets facilitated the identification of network composition (e.g., Twitter users by type and location) as well as the evaluation of government and nonprofit agency activity. Analysis of network data provides an assessment of the patterns of interaction that occurred between Twitter users from an incident’s onset through the following 6 days. Network data were analyzed using the social network analysis software NodeXL (Smith et al., 2010) and UCINET (Borgatti, Everett, & Freeman, 2002). NodeXL was used to calculate network statistics (e.g., in-degree and out-degree centrality). In-degree centrality represents the number of times a user was retweeted. A user with a high in-degree value 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.10 A user with a high out-degree score would be passing along several messages from external sources. Findings Boston, MA—Bombing/Terrorism Event On April 15, 2013, two improvised explosive devices were detonated close to the Boston Marathon finish line. Three people were murdered and hundreds injured.11 First responders mobilized to treat mass casualties and law enforcement commenced an extensive investigation and manhunt. National media descended, and Twitter users chimed in.


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The trending topic #bostonstrong was one of a handful of popular Bostonrelated topics on Twitter and was rank-ordered higher than the others for days. Other hashtags included #bostonbombing, #watertown, #boston, #bostonpolicescanner, and #oneboston. Upon analysis of the data, it was revealed that #bostonstrong was an arena in which to express goodwill and condolences to the City of Boston rather than a platform to bolster response activities. Regardless, examining government and nonprofit participation or the lack thereof within this network is important because #bostonstrong was the highest ranked and thus was the most used hashtag during the days immediately following the event. Table 2 lists the frequency distribution of Twitter users who participated in #bostonstrong by type and geographic location. Government (0.35 percent) and nonprofits (0.83 percent) comprise the smaller share of the information network by far. Of the government agencies, neither major institutions nor elected officials were represented; only local schools, staffers from outside of Boston, and one foreign government participated. No local or Massachusetts-based nonprofit took part and no users represented organizations with emergency management missions, other than a public affairs official from South Carolina’s Red Cross. Figure 1 illustrates the patterns of interaction formed by users who retweet messages. Several separate groups, called components (see Scott, 2005, p. 101), illustrate #bostonstrong as a fragmented information network; in the absence of the hashtag, few users would have been aware of the variety of messages. In Figure 1, separate components are distinguished by color of node. No government or nonprofit agency appeared as a prominent network participant, given the in-degree and out-degree centrality scores. The three government entities represented in the dataset were entities in other states and were not associated with emergency management in any way. Additionally, they had in-degree scores of two or less, suggesting that the information they put into the network was not shared. The 28 nonprofits represented the only agency with an in-degree score above 1 had little to do with the response operation. This data suggests that typical emergency management based nonprofit agencies and government agencies generally were not involved in the highest trending hashtag network, #bostonstrong. Table 2. Frequency Distribution of Twitter Users Participating in #bostonstrong by Type and Location Local

Government Media Nonprofit Commercial Private citizen Total

In-State

Out-State

International

Total

N

%

N

%

N

%

N

%

N

%

11 13 15 22 538 599

0.25 0.30 0.35 0.51 12.41 13.81

1 5 1 16 223 246

0.02 0.12 0.02 0.37 5.14 5.67

2 69 16 113 2,722 2,922

0.05 1.59 0.37 2.61 62.78 67.39

1 18 4 19 527 569

0.02 0.42 0.09 0.44 12.15 13.12

15 105 36 170 4,010 4,336

0.35 2.42 0.83 3.92 92.48 100

1,481 private citizens who participated did not share their location on Twitter and are not included in this analysis.

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Figure 1. Diagram of Twitter Information Network #bostonstrong.

Regardless, many agencies used social media to communicate but used less efficient means than appropriate hashtags. When looking at the total list of government and nonprofit agency tweets, the Boston event had the largest number, as compared to other events in this study, implying that government and nonprofit agencies played the largest role in Twitter communication networks in regard to the Boston event. However, what our analysis revealed was that not only were there multiple hashtags in use, and thus multiple networks that did not generally overlap, but also that the use of specific hashtags in relation to the Boston event was not immediate. Instead, the hashtag #bostonmarathon, used for the event before the bombing, was applied by a number of nonprofit and government agencies for tweets relating to the emergency event on the day of the event and immediately following it. These same agencies continued to post information relating to the event, but did so without using any hashtags. Despite a number of tweets by nonprofits and government agencies the day of the bombing, the first event-related hashtag was #prayforboston by the Salvation Army of Massachusetts. Other organizations, however, chose not to adopt it. Meanwhile, the hashtag #OneBoston was used often by Boston Mayor Tom Menino starting on the day after the incident, but did not appear to catch on with other agencies either. Perhaps the best hashtag to use in terms of disseminating information widely would have been to stick with #bostonmarathon, especially since another trending hashtag, #bostonbombings was never used by any government or nonprofit organizations in the dataset, although it was quite popular with the public at large. The Twitter use by the identified agencies during this event was rather high as compared to that of other events. At the national level, 6 of President Obama’s 23 tweets (26 percent), 3 of FEMAs 6 tweets (50 percent), and 23 of the Salvation Army’s 43 tweets (53 percent) in the time frame were relevant to the event. Meanwhile none of the 220 tweets made by the United Way’s national level Twitter accounts during the 7 days were relevant to the events in Boston. All of


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FEMA’s relevant tweets during this time period were retweets of other users, as were half of the tweets by the Salvation Army. This implies that FEMA was adding no unique information to the network. At the state level, 62 of the 99 tweets by the Massachusetts Emergency Management Agency (63 percent) and 17 of the 28 tweets by the Salvation Army of Massachusetts (61 percent) were relevant to the event. Despite the similar percentage of relevant tweets, the two organizations provided a vastly different amount of information. Looking at the retweet data, of the relevant tweets, 76 percent of those by the Salvation Army were newly created information as compared to the 52 percent by the Massachusetts Emergency Monument Agency. This implies that the state level nonprofit rather than the government agency was providing a more unique message to the Twitter network, while the government agency was serving more of a relay role of information from other sources. At the local level, the relevant tweets by the Boston City Council, Boston EMS, Boston FD, and Boston PD were 1 of 3 (33 percent), 46 of 112 (41 percent), 13 of 30 (59 percent), and 135 of 159 (85 percent), respectively. The only local nonprofit of interest to provide relevant tweets during the time frame was the Boston Chamber of Commerce, with 13 of its 30 tweets (41 percent) related to the event. Given the percentage and number of relevant tweets, the Boston Police Department would seem to be the best source of information about the event for the public. Some agencies, most notably the Boston Police Department, used Twitter to appeal directly to the people and asked them not to post certain things on Twitter because of operational security. One such tweet specifically addressed social media use during the manhunt and possibly influenced the behavior of private citizens. Many people across the Boston metro area were using social media to post photos and comment on the operations of law enforcement personnel. On April 19, the Boston Police Department issued the following tweet using the hashtag #MediaAlert: “WARNING: Do Not Compromise Officer Safety by Broadcasting Tactical Positions of Homes Being Searched.” This tweet was covered by national and state media outlets, thus amplifying its message. Within a week, it had been retweeted 20,060 times and received 1,265 favorites. West, TX—Explosion/Technical Disaster On April 17, 2013, a fertilizer plant exploded in West, TX, located on Interstate 35 just north of Waco, a town of about 2,800 people. Fourteen people died, 12 of whom were first responders (firefighters and paramedics) who had responded to the initial fire prior to the massive explosion. As first regional and then national media descended on the scene, a video of the explosion shot by motorist Derrick Hurtt was posted on YouTube and quickly went viral.12 The massive explosion caught the nation’s attention, and Twitter users went online to communicate. The case of West, TX supports hypothesis 1, for the actors in the hashtag network were not limited to local users, but came, instead, from across the state

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and the nation. Local tweets only accounted for 4.88 percent of the network while 46.22 percent and 41.69 percent of the tweets were from other users around the state and the nation, respectively. Like #bostonstrong, private citizens made up the vast majority of participants in #westexplosion—76.85 percent. Media entities comprised 15.9 percent, while government and nonprofit entities made up only 0.45 and 3.3 percent of the hashtag network, respectively (see Table 3), suggesting support for hypothesis 3. While the media integrated the information network serving as core hubs, a variety of nonprofit organizations (96) added message content and substance. The Texas Volunteer Organizations Active in Disaster (VOAD) and the Houston VOAD, for example, tweeted eight messages using #westexplosion. Their message content included public information (e.g., the location of recovery centers) and praise for other nonprofits involved in the response operations. However, their in-degree scores of 3 and 1, respectively suggest that very few people retweeted their posts. The local chapter of the American Red Cross, Heart of Texas, however, was retweeted by eight different participants, including media sources. The chapter posted messages about the opening of a resource center and information on a community-wide memorial service. Three other Red Cross chapters from Texas also participated. Central Texas Red Cross, for example, tweeted “Need medications replaced after #westexplosion visit @RedCross at the Community Center.” This message was retweeted by three users. Other nonprofits used the hashtag stream to fund raise for the victims and to thank major donators (e.g., Texas Salvation Army). The Heart of TX chapter of the Red Cross made use of the #westexplosion hashtag and also tweeted information and passed on information from others about the event using the hashtag consistently. This provides limited support to hypothesis 2 regarding the important role of a nonprofit actor. With respect to government agencies, only 13 government-related actors participated in #westexplosion. Of those, most were locally elected officials from outside the affected area. There was a noticeable lack of participation by state and other agencies.13 One local agency that was involved in response operations, Table 3. Frequency Distribution of Twitter Users Participating in #westexplosion by Type and Location Local

Government Media Nonprofit Commercial Private Citizen Total

In-State

Out-State

International

Total

N

%

N

%

N

%

N

%

N

%

1 34 8 20 79 142

0.03 1.17 0.27 0.69 2.71 4.88

9 291 54 51 941 1,346

0.31 9.99 1.85 1.75 32.31 46.22

3 127 30 26 1,028 1,214

0.1 4.36 1.03 0.89 35.3 41.69

0 11 4 5 190 210

— 0.38 0.14 0.17 6.52 7.21

13 463 96 102 2,238 2,912

0.45 15.90 3.30 3.50 76.85 100

248 private citizens who participated did not share their location on Twitter and are not included in this analysis.


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however, participated. The West Independent School District tweeted, “Rumor control. No gas leak at WES. Licensed Plummer has certified that lines are good! #westexplosion.” This message was retweeted several times and at least once by a local television station. This provides additional support for hypothesis 3, showing a limited role for government agencies in the information network. Figure 2 demonstrates the central role of media outlets in integrating the information network. The information network is integrated by interconnected media outlets that tie in individual users into a larger-core component. Meanwhile, the Heart of Texas Red Cross, American Red Cross, and Texas Governor Rick Perry all made use of the hashtags #west and #westtx rather interchangeably. Most of this was due potentially to the vast amount of retweeting, but Heart of Texas appears to have used a mix of all three hashtags in tweets they originated. During the time period, the United Way of Texas tweeted often and used hashtags, but only once referenced the events in West, TX and used the hashtag #westtx. It is not clear from this data if the Heart of Texas Red Cross use of multiple hashtags led to confusion or assisted in getting the information out to more users. Perhaps a future analysis which looks at all three hashtag datasets can answer that question. The Twitter use by the identified agencies during this event was rather low; this incident seems to have gained the least amount of national Twitter attention. None of President Obama’s 19 and only 1 of FEMAs 14 tweets during the time frame were relevant to the event. Even among nonprofit agencies, there was low levels of attention; none of the 182 tweets by the United Way were relevant and only 11 of the 48 (23 percent) tweets by the Salvation Army were relevant. State and local level government activity was also low, despite including tweets by Governor Perry. Four of his nine tweets (44 percent) were related to the event. Meanwhile, 20 of the 33 tweets (61 percent) by the State of Texas official Twitter

Figure 2. Diagram of Twitter Information Network #westexplosion. Isolates removed.

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account were related to the event, and the one tweet made by the Waco McIennan Office of Emergency Management was not even related to the event.14 The Heart of Texas Red Cross, however, was an important provider of information, especially when this is combined with its extensive use of hashtags; 73 of its 103 tweets (71 percent) were related to the event. Peoria, IL—Flooding/Natural Disaster On April 17, 2013, strong storms poured 2.47 inches of rain on Peoria, IL and even more on the watershed down river. Over the next few days, more rain fell, swelling the Illinois River. Volunteers mobilized to reinforce the city’s levees with sandbags, but flood waters soon exceeded fortifications. On April 23, 2013, the river crested at 29.35 feet, 14.35 feet above flood stage.15 Hundreds of families were displaced, and entire business districts were flooded. Unlike the abrupt, fast developing nature the Boston and West, Texas incidents, the massive spring floods in the Midwest took longer to develop and towns across multiple states and counties were inundated; the slower development of the even lessened the potential for national attention. Peoria and other Midwestern towns did receive some national attention, but by in large, these disasters failed to capture a wide audience. Perhaps in part to the greater attention given to Boston and West, Peoria failed to fuel a trending topic. Many local Twitter users, albeit in fewer numbers, still communicated about the situation using an established hashtag, #peoria, as a forum to discuss and share information on the floods. The case of Peoria, IL supports hypothesis 1; the actors in the hashtag network were not limited to local users, but instead, came from across the state and the nation. However, the geographic differences were not as extreme in this case as others, for 30.45 percent of the tweets came from local users, 33.56 percent from in-state users and 33.22 percent from users around the country (see Table 4). Results from Peoria also supports hypothesis 3; private citizens made up the majority of participants (60.90 percent), media comprised 20.42 percent, and government and nonprofit agencies comprised only 2.42 and 4.84 percent respectively. The network map in Figure 3 shows that rather than having one Table 4. Frequency Distribution of Twitter Users Participating in #peoria by Type and Location Local


In-State

Out-State

International

Total

N

%

N

%

N

%

N

%

N

%

3 33 6 10 36 88

1.04 11.42 2.08 3.46 12.46 30.45

4 12 2 9 70 97

1.38 4.15 0.69 3.11 24.22 33.56

— 13 6 11 66 96

— 4.50 2.08 3.81 22.84 33.22

— 1 — 3 4 8

— 0.35 — 1.04 1.38 2.77

7 59 14 33 176 289

2.42 20.42 4.84 11.42 60.90 100.00



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Figure 3. Diagram of Twitter Information Network #peoria. Isolates were removed.

large network component as seen in the West, TX case in Figure 3, there were multiple, disconnected groups. This is likely in part due to the small number of tweets in this network and the lack of a major network hub or set of hubs to integrate the network. National level activity regarding Peoria was almost nonexistent during the week of the flooding with no relevant tweets made by the President or FEMA and only one made by the Salvation Army and the United Way. The local Red Cross chapter, Central Illinois, was the most active of the 14 nonprofits that used #peoria, sending out original information and redistributing messages they felt were valuable. They were also an active twitter user with 44 posts during the week of the flood; 31 (70 percent) were directly related to the extreme events. Sixty-one of the relevant tweets were retweets, suggesting that the agency was passing along messages more than creating its own content for the public. Like the other hashtag streams examined in our research, there was very limited participation by government-related actors. Governor Pat Quinn played a role in the network by using #peoria and having his message retweeted. While his message was only retweeted three times in the data set, he was a relatively important entity within the limited network. In addition, 44 percent of the governor’s 32 tweets made during the week of the flooding were directly related to the incident. Senator Mark Kirk tweeted once. This finding provides at least partial support for hypothesis 1, demonstrating a limited yet important role, but does not offer as much support for the hypothesis as expected. While no state or local government agencies traditionally associated with emergency management functions used the hashtag #peoria, they were active in the larger twitter network in other ways. The Illinois Emergency Management Agency tweeted 26 times during the week of the flooding, and all of the tweets were directly related to the event. Multiple local government agencies used

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Twitter, but relevant information was not often tweeted. For example, of the nine tweets by the local Sherriff’s Office, only 1 was flooding related; of the 21 tweets by Peoria Health Department only 4 were related, and of the 22 posts made by Peoria County, only 3 were related. On the local level, the best source of information was Scott Sorrel, an Assistant County Administrator, who used his personal account to pass along information; he retweeted 19 times during the week of the flood, 11 of which were related to the event. Moore, OK—Tornado/Natural Disaster A month following the Boston, West, and Peoria disasters, an ESF 5 tornado ripped through Moore, Oklahoma, a suburb of Oklahoma City, on May 20, 2013. Twenty-three people were killed and hundreds more were injured. Regional and national media captured images of teachers leading children out of decimated elementary schools and a town struggling to come to terms with the devastation and rebuilding. The trending topic on Moore was based on a “prayfor” phrase that had been used for other topics. #prayforok provided a mechanism for users to comment on the event. It does not appear, however, to have been used for response or recovery purposes. Private citizens made up almost the entire set of participants in this network; government entities played no role, and the 13 nonprofit entities represented only 1.11 percent of the network. The network map in Figure 4 shows that there were multiple, small disconnected groups rather than having one large component as seen in the West, TX case. Additionally, the role of nonprofit agencies was minimal; Table 5 includes no government or nonprofit actors. The only support provided to hypothesis 1 is the concept that these entities roles will be limited.

Figure 4. Diagram of Twitter Information Network #prayforok. Isolates removed.


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Table 5. Frequency Distribution of Twitter Users Participating in #prayforok by Type and Location Local


In-State

Out-State

International

Total

N

%

N

%

N

%

N

%

N

%

0 1 0 0 35 36

— 0.09 — — 2.99 3.08

0 1 1 0 198 200

— 0.09 0.09 — 16.94 17.11

0 13 12 9 863 897

— 1.11 1.03 0.77 73.82 76.73

0 1 0 0 35 36

— 0.09 — — 2.99 3.08

0 16 13 9 1,131 1,169

— 1.37 1.11 0.77 96.75 100


The case of Moore, however, does conform to hypothesis 3. The actors in the hashtag network were not limited to local users, but came from across the state and the nation, instead. Local actors only accounted for 3.08 percent of the network, while 17.11 percent and 76.73 percent of the total actors were from other users around the state and the nation, respectively. Few nonprofits and government agencies used the hashtag #PrayforOK. The religious nature of “pray for” may have caused some government agencies to avoid it; however, Oklahoma Governor Mary Fallin and Lt. Gov. Todd Lamb both used a similar #prayforoklahoma hashtag. No other government or nonprofit user adopted this hashtag variant. The national-level Salvation Army twitter used the hashtag #prayersforoklahoma, but it was not noted, possibly due to using “prayers” rather than “pray” and the solitary use of the hashtag. The City of Moore used the hashtag #oklahomastrong multiple times and the City of Moore and OK.gov both used #moorestrong once. Both of these are a takeoff of the #bostonstrong hashtag that was widely successful as a celebrity-driven stream following the marathon bombing. The city did not stick to a single hashtag, and the city and state levels did not appear to coordinate their efforts well despite the use of similar hashtags. This suggests that the resources and the knowledge of best practices existed, but coordination between organizations was lacking. The lack of participation in the hashtag network does not suggest a lack of participation in the larger twitter network. The Moore, OK event received a fair amount of attention across national, state and local levels. On the national level, 5 of President Obama’s 29 tweets (17 percent) in the time frame were related to the event, as were 40 of FEMA’s 56 tweets (71 percent) The Moore tornado received the most attention from the government as a whole in the Twittersphere. Moore also received the most attention from national nonprofits with 94 of the Salvation Army’s 109 tweets (86 percent) and 11 of the United Way’s 35 tweets (31 percent) during the time frame concerned the extreme event. Moore also saw sizeable state level Twitter attention from both government and nonprofit actors. Governor Mary Fallin tweeted 108 times during the week following the tornado; 74 (69 percent) of her tweets were related to the event. In addition, Moore was the only event where relevant tweets by the Lt Governor were seen; 8 of his 11 posts (73 percent) were related to the tornado. The state’s

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official twitter account also had a 73 percent relevancy rate with 51 of its 70 tweets relating to the tornado. State level nonprofits were also well represented and relevant in Oklahoma; 20 of the Salvation Army’s 31 tweets (65 percent), 40 of the Red Cross’ 162 tweets (25 percent), and 16 of the United Way’s 90 tweets (19 percent) were related to the tornado. Local level information in Moore was lacking; the Moore Emergency Management Agency only tweeted four times in the relevant time frame, and none of the posts were related to the incident. The 45 relevant tweets by the City of Moore may have been lost among the 194 total tweets over the week. Overall Trends Our research finds relatively limited levels of nonprofit and government involvement in the cases examined. While some nonprofits such as the American Red Cross contributed substantive information to the network, government agencies, for the most part, failed to exploit trending topics to penetrate otherwise disparate groups of constituents. This lack of interaction represents missed opportunities for self-organization. Across all four events, there is strong evidence for hypothesis 1, for government and nonprofit participants are not limited to local jurisdictions but rather span across local, state and national jurisdictions. Evidence from individual nonprofit and government agency Twitter pages indicate that state and regional level organizations in particular did indeed have a presence on Twitter. Greater participation on the Red Cross’s part, for example, could be due to their national social media strategy, operationalized through their Digital Operations Center. Monitoring and adopting trending topics to existing hashtag streams would assist other agencies in expanding their reach during an extreme events. The data fails to support hypotheses 2 and 3. Despite the somewhat active role of government and nonprofits on Twitter during extreme events, their role in hashtag networks appears quite limited. When these agencies did appear, they often had low in-degree and out-degree scores; this implies that their messages were not regularly retweeted and they did not pass on information from other users in the network. The overall share of nonprofit and government agencies in terms of percentage of network composition was relatively low. Table 6 offers the number Table 6. Twitter Users by Type and Incident Boston, MA Bombings West, TX Explosion Peoria, IL Flooding Moore, OK Tornado


N

%

N

%

N

%

N

%

15 105 36 170 4,010 4,336

0.35 2.42 0.83 3.92 92.48 —

13 463 96 102 2,238 2,912

0.45 15.90 3.30 3.50 76.85 —

7 59 14 33 176 289

2.42 20.42 4.84 11.42 60.9 —

0 16 13 9 1,131 1,169

— 1.37 1.11 0.77 96.75 —


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of Twitter users by type and incident and indicates that the vast majority are private citizens. Because of the high percentage of private citizens, there are ample opportunities for nonprofit and public officials to disseminate key pieces of information as well as to monitor for new information or emergent behavior. As argued in the previous sections, however, the extent to which government agencies participate in these networks is in question. Limited government involvement in the four cases examined here was underlined by the fact that most of these actors were merely local elected officials from unaffected areas, and government agencies seldom shared information related to response efforts. In contrast, regional American Red Cross chapters and other nonprofits not only made up a greater percentage of participants than government agencies, but they also substantively contributed to the information network. An examination of the tweets by nonprofit and government agencies, separate from the hashtag streams, provide multiple insights to how these organizations used Twitter during the time frames. In less populated locations such as Peoria, IL, Moore, OK, and West, TX, nonprofits and government agencies were less likely to have twitter accounts, compared to Boston, MA. However, even among the three smaller locations, Twitter use varied greatly. Moore, OK had the most local representation, Peoria, IL had a small amount, and West, TX had virtually none. Only in Boston and Moore were a variety of organizations active on Twitter across local, state and national levels. While national-level organizations tweeted often, state-level organizations tended to provide more tweets relevant to the event in question during the time period. The lack of local representation could be due to organizational resource deficiencies, but given that Moore, OK had multiple local organizations using Twitter, there may be other factors at play. Of particular note is the somewhat dependable amount of relevant information that comes out of state level agencies. For example, in the three cases where governors maintain Twitter accounts, 44–69 percent of their posts in the days following the extreme event were related to the event, as compared to the President, whose posts were only related in two of the four cases; the percentage of related posts was only 17–26 percent. Additionally, at the county and local levels, when the Red Cross and Salvation Army maintain Twitter accounts, they tend to provide a larger and more relevant amount of information than do government agencies. Boston was the one outlier where local agencies appear to be dependable sources for tweets related to an extreme event. In regard to hashtags, use seems rather sporadic. Many national level organizations use hashtags, but they do not seem to have made much use of the ones identified in this data set. Instead, organizations like FEMA used general terms such as #flooding in reference to the Peoria event, whereas Peoria used a more general hashtag, #peoria; even Illinois Governor Pat Quinn, Senator Mark Kirk, the IL Senate Democrats, and the Central Illinois Red Cross had this general hashtag. These organizations also used the hashtag @peoriawx at times, leaving a question of why they did not stick to the same hashtag for all tweets. Additionally, there is no hashtag use at all by the Illinois Department of Emergency Management, the Peoria Health Department, the Peoria County

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Government, the Salvation Army of Peoria, or the Peoria Chamber of Commerce, despite multiple tweets about the event by these organizations. This suggests that the resources exist, but the knowledge of best practices of social media uses do not. Discussion Successful emergency management requires the creation of a common operating picture that accounts for multiple actors and system types (Comfort, 2007b). Network analysis provides a tool to evaluate the extent to which nonprofit and government agencies participate in social media-based information networks during disasters. Scholars and practitioners alike have searched for alternative modes of communication to facilitate decision-making under conditions of uncertainty (Comfort, 2007b; Mendonça & Fiedrich, 2006; Sykes & Travis, 2012). Twitter appears to be well-situated to aid in decision making and facilitate self-organization in part due to its scale-free architecture. Hashtag streams and trending topics connect entities across social sectors and political boundaries, providing the type of boundary spanning capabilities long anticipated by scholars of information technology (Fountain, 2001). All of this is not to argue that Twitter provides the necessary components to manage extreme events by itself—far from it. Disasters are complex events and require insight from multiple perspectives (Mileti, 1999; Sylves, 2008). Twitter provides one piece of a larger tool set with growing potential for the future. Information communication technology such as radios, cell phones, email, and specially designed decision support platforms currently provide the majority of formal mechanisms for information exchange since they are more established within the emergency service disciplines. However, along with the increasing use of technology, devices such Twitter are already found on an array of devices, and similar technology may become an increasingly important part of our daily lives. There are still constraints that impede further reliance on Twitter as an organizing tool. Twitter does not reach everyone, although the company estimates that 200 million people use the platform worldwide, predominantly the younger generations (Edwards, 2013). Reverse 911 land-line telephone calls and micro-targeted Short Message Service texts to cell phones continue to provide government agencies with more reliable communication tactics. Also, monitoring Twitter for public feedback, crafting content, and delivering it requires manpower and a minimal degree of technical expertise which may depend on resources that many smaller agencies might be unwilling or currently unable to commit. The data here support the statement that resources play a role in government and that nonprofit agencies using Twitter as larger organizations and organizations in larger cities were more likely to have twitter accounts. While smaller organizations might rationalize that they should not fully employ social media due to a lack of resources, small commitments can be made that


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represent low cost and low intensity activities. Almost all local organizations and local leaders have the resources necessary to send out a few tweets over the course of the day to provide valuable and timely information to the public; rather, they might choose not to, possibly because of being unfamiliar with the medium. With respect to hastags, several factors might explain the lack of participation by government and nonprofit agencies. Personnel tasked with implementing agency social media may not be familiar with the convention or with tactics employed to identify trending or relevant topics. Personnel may decide not to participate in emergent hashtag topics until an appropriate tone has been established. Furthermore, certain hashtag communities may be considered outside the scope of a public agency (see #prayforok). Further analysis is necessary to determine why certain organizations choose to embrace social media while others do not. Additionally, the extent to which agencies perceive that their constituents use social media and the available resources, particularly in terms of manpower, may influence nonprofit and government agencies decisions regarding the use of tools such as Twitter. Recommendations for Future Research This article analyzes only one hashtag stream per disaster and does not capture the full range of information networks that manifest during any given large-scale incident. In three of the cases presented, other trending topics emerged. Analysis of individual nonprofit and government agency Twitter activity also indicate that organizations created their own hashtags that did not garner sufficient attention to warrant a trending topic designation. Future research should evaluate the diversity of hashtags that emerge and gauge the extent to which the Twitter community participates. Research that evaluates network composition, structure, and message content across multiple hashtags would be particularly useful. While this article suggests that the type of hazard, especially the speed at which an incident develops and terminates, influences the generation and use of hashtags, more research is needed. It is expected that quick onset events receive significant national attention and will generate larger hashtag streams. In the case of Peoria, Illinois, the disaster was relatively slower to unfold and was distributed across states and counties, failing to garner much attention on Twitter. It is expected that a slower onset of events, if significant attention is generated at all, will create smaller hashtag streams that extend over a longer period of time but do not generate trending topics status. Finally, there should be investigation into the extent to which message traffic within disaster hashtag streams provide value to affected parties. This article focuses on the composition and structure of the hashtag networks. Additional research could focus on the content of messages and evaluate the extent to which they add value to both response agencies and the affected population. Several studies have examined the message content of predominately private citizen-based

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networks. However, these studies fail to critically evaluate the utility of messages to the response community. Clayton Wukich is in Department of Political Science, Sam Houston State University, 1901 Avenue I, Rm 481, Huntsville, Texas, 77341, USA Alan Steinberg is in Department of Political Science, Sam Houston State University, 1901 Avenue I, Rm 481, Huntsville, Texas, 77341, USA

Notes The authors thank Derek Mayrant, Franklin Leung, and David Pratt for coding the data and Johnny Nguyen and Danika Wukich for their technical guidance. The authors are also indebted to Dr. Marc A. Smith and his team for access to NodeXL and their guidance. 1. Wukich and Robinson (2013) apply Burt’s (2005) brokerage and closure framework to leadership strategies in emergency management. See also Andrew and Carr’s (2013) study of bonding and bridging strategies used during regional preparedness activities. 2. The Enhanced Fujita (EF) scale is used to measure wind strength. EF five is the highest classification, indicating wind speeds above 200 mph. 3. Because #peoria proved to be a regularly used hashtag category, messages that did not have to do with flooding were removed. 4. Users can mark their profiles as private so that their tweets are not available via public search. Access is granted only to those who have been given specific permission to follow the user. Therefore, these tweets cannot be included in this dataset. NodeXL’s importation tool using Twitter’s Search API. This API limits the data gathering to the most recent tweets containing the searched for hashtag. As Twitter says, “The Search API is not complete index of all Tweets, but instead an index of recent Tweets. At the moment that index includes between 6 and 9 days of Tweets” (https://dev.twitter.com/docs/using-search). This API focuses on relevance rather than completeness and this means that some tweets and users may be missing from this data set. Additionally, content from private, hidden, suspended or deleted users is not captured. The use of a small limited range, as done in this study, helps reduce potential data loss. 5. Location was derived from users’ volunteered data. Many cases, predominantly private citizens, existed in which coders were unable to ascertain user location. Two coders worked collaboratively from the same set of definitions. A third reviewed and revised any discrepancies to ensure reliability. Individual users were coded as representatives of their organization, especially media, if in their user description they posted their job title and the content of their message(s) was related to their position/organization. 6. This article focuses on agencies charged with response duties. Future work could investigate public agencies such as the National Weather Service and how they contribute to the larger information networks. 7. Data was also gathered but is not reported on some nonprofits not traditionally involved with extreme events such as the Rotary, Exchange and Lion’s Club. While these agencies are active users of Twitter on the national level, state and local Twitter accounts were rare and usage was almost non-existent. In addition, despite the large number of tweets during the events, relevant tweets ranged from 0 to 10 percent across all of these accounts suggesting that the relevant information would have likely gotten lost among the large amounts of unrelated activity. Further analysis of these specific types of organizations and their disaster communication is outside the scope of this study. While the Chamber of Commerce was initially also left out, it was re-included as a representative of business rather than personal interests. 8. Data available upon request. 9. The concept of looking at retweet counts allows for an analysis of the degree by which an agency is acting as a distributor or creator of relevant information. Because there is virtually no prior analysis of this type of data in regard to government and nonprofit agencies in events, the analysis reported here is exploratory at best.


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10. In this network, the out-degree metric counts the number of others that the user in question retweeted. In other words, 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, that is, 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. 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 implies that the user created content but that the 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 if they are private users with few followers such as a small personal network. 11. Later, a Massachusetts Institute of Technology police officer Sean Collier was murdered during the manhunt. 12. Explosion in West, Texas - Derrick Hurtt video. Retrieved April 18, 2013. 13. The Texas Department of Transportation used #westexplosion to announce that state office would be closed. 14. At the time of the event, the Texas Office of Emergency Management did not have a Twitter account, and only 1 of the 18 tweets (13 percent) made by the Texas Department of Public Safety were related to the event. 15. Peoria Journal Star. Timeline of historic 2013 flood. Retrieved September 19, 2013

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