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Informing crisis communication preparation and response through network analysis:
An elaboration of the Social-Mediated Cr...
Informing crisis communication 2
Informing crisis communication preparation and response through network analysis:
An elab...
Informing crisis communication 3
organization or a third party) affect publics’ cognitive, affective and behavioral respon...
Informing crisis communication 4
Himelboim, Golan, Moon and Suto (2014) employed the relationships identified by
Twitter e...
Informing crisis communication 5
interpretation because whether a group of people understands that it shares an interest a...
Informing crisis communication 6
facilitate crisis information sharing, opinion sharing, and emotional expression in times...
Informing crisis communication 7
As represented in Figure 1, solid arrows represent direct relationships in the flow of cr...
Informing crisis communication 8
Research on social media from a social network perspective shifts the focus from individu...
Informing crisis communication 9
holes identified individuals and organizations in unique positions in a network, where th...
Informing crisis communication
10
the context of organizational relationships with stakeholders, social mediators play a k...
Informing crisis communication
11
remaining disconnected from one another. This structure reflects a public that receives ...
Informing crisis communication
12
communication when quick and broad dissemination of information is paramount. The nature...
Informing crisis communication
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@AmericanAir) and its main hashtag (e.g., #AmericanAir). For purposes of standardization...
Informing crisis communication
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portion of reciprocal relationships (e.g., user A mentioned user B, and user B mentioned...
Informing crisis communication
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Classification of social mediators. We classified Twitter users into types of social
act...
Informing crisis communication
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few occasions where #united captured the Manchester United sports team tweets, although ...
Informing crisis communication
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Adjusted R2
=.63). See Figure 3. For examination of social mediators’ type by airline, s...
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(@americanair, #americanair). In Figure 5, circles represent users and the connecting li...
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SMCC model. They contribute to practice through identification and parsing of public typ...
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Frontier airlines, both identified as “small” by the parameters of this study, showed th...
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categories for U.S.-based airlines and believe those categories will carry to other indu...
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References
Austin, L., Liu, B. F., & Jin, Y. (2012). How audiences seek out crisis infor...
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Coombs, W. T. (2012). Ongoing crisis communication: Planning, managing, and responding
(...
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Jin, Y., Liu, B. F., & Austin, L. L. (2014). Examining the role of social media in effec...
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Newman, M.E.J. (2004). Detecting community structure in networks. The European Physical
...
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Figure 1: Social-Mediated Crisis Communication Model (Jin, Liu, & Austin, 2014)
Figure 2...
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Figure 3: Type of social mediator type by airline size
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Figure 4: Social mediator type by airline
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Figure 5: The American Airlines social network (data collected on 1-27-15)
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  1. 1. Informing crisis communication preparation and response through network analysis: An elaboration of the Social-Mediated Crisis Communication model Abstract To test and elaborate as necessary the Social-Mediated Crisis Communication (SMCC) model’s key publics classifications (Liu et al., 2012) and to provide practical insight to public identification for crisis communication planning and response, this study uses network analysis to identify social mediators (Himelboim et al., 2014) and clustered publics in airline Twitter networks. In our analysis, social mediators and network clusters are classified according to the publics taxonomy of the SMCC model. The characteristics of the social mediators and the network structure of the clusters are also identified in airline Twitter networks. Our findings suggest further elaborations and more in-depth identification of key publics in social-mediated crisis communication. Keywords: Social-Mediated Crisis Communication model, Crisis Communication, Social Network Analysis, Twitter, Social Media, Social Mediators.
  2. 2. Informing crisis communication 2 Informing crisis communication preparation and response through network analysis: An elaboration of the Social-Mediated Crisis Communication model Introduction The development of social media has made crisis communication more complicated (Coombs, 2012). Liu, Jin, Briones and Kuch (2012) argued that the first step in managing a blog-mediated crisis is to identify bloggers who are influential with key publics (p. 357). Jin and Liu (2010) developed a matrix as a means to predict which blogs would be most influential in a crisis. Cho and Cameron (2006) noted “the importance of Internet community and Netizens as organized and influential publics” (p. 199) and suggested that Internet communities be considered as an additional external public in public relations theory building. While blogs and their relationship to crisis communication has been examined by several scholars, Liu et al. (2012) found that public relations practitioners believed Twitter and Facebook were more useful crisis communication tools than were blogs (p. 366). In response to their findings, they proposed the Social-Mediated Crisis Communication model, which identifies the flow of crisis information online and offline, created, shared, followed, and consumed by influential publics, and how it interacts and influences an organization’s crisis management strategies and responses to those publics in varied forms and sources, according to different crisis types. To date, the SMCC model has been tested through interviews and experiments in both organizational crisis and disaster situations to explore how SMCC components such as crisis origin (e.g., emanating from the organization or from an outside entity), crisis information form (i.e., traditional media, social/new media, or word of mouth), and crisis information source (i.e.,
  3. 3. Informing crisis communication 3 organization or a third party) affect publics’ cognitive, affective and behavioral responses to crisis or disaster information, their information seeking and sharing patterns, as well as their behavioral tendencies such as acceptance of crisis response strategies (Austin, Liu, & Jin, 2012; Liu, Austin, & Jin, 2011; Liu, Jin, & Austin, 2013; Jin, Liu, & Austin, 2014; Jin, Liu, Anagondahalli, & Austin, 2013) and their likelihood of following proactive action instructions (Liu, Fraustino, & Jin, 2015a, 2015b). One important component of the SMCC model is yet to be fully developed conceptually and tested empirically: key publics in social-mediated crisis communication. So far, three key publics, who seek, produce, or share information before, during, and after crises, have been identified in the SMCC model (Liu et al., 2012; Jin, Liu, & Austin, 2014): 1) Influential social media creators who develop and post crisis information online; 2) Social media followers who consume this information from social media creators and also share this information both on and offline; and 3) Social media inactives who do not participate actively in the social media, but receive this crisis information via other channels—including traditional media and word-of mouth communication—from social media followers, creators, or other inactives. One challenge of testing the SMCC key publics classifications lies in measurement. Given the tenet of the model itself, which is the crisis information flow based upon the influence exerted by the interconnected publics, the fuller picture and further operationalization of these SMCC key publics need to be not only examined at the individual public level but also at the relationship level (i.e. organization-public relationship and how one public is in relations with other publics). This concern echoes Ledingham’s (2006) argument that “The central unit of measures of public relations success is the organization-public relationship” (p. 475), when discussing relationship management as a general theory of public relations.
  4. 4. Informing crisis communication 4 Himelboim, Golan, Moon and Suto (2014) employed the relationships identified by Twitter exchanges to identify “social mediators”. They defined social mediators as “the entities which mediate the relations between an organization and its publics through social media” (Himelboim et al., 2014, p. 367). These scholars also identified clusters that are formed by subgroups of users who engage with one another more than with others. Social mediators bridge an organization with clusters of users, or publics, that are not directly engaged with the organization. Therefore, to test and elaborate as necessary SMCC’s key publics classifications and to provide practical insight to public identification for crisis communication planning and response, this study uses network analysis to identify social mediators (Himelboim et al., 2014) and clusters in airline Twitter networks. By its nature, the airline industry is crisis prone. The industry is also known to be effective in using social media to monitor and respond to consumer comments (Authors, in press). The combination of these characteristics makes the airline industry a perfect candidate for employing network analysis to test and elaborate the Social- Mediated Crisis Communication (SMCC) model. In our analysis, social mediators and network clusters are classified according to the publics taxonomy of the SMCC model (Liu et al., 2012). Our findings suggest further elaborations and more in-depth identification of influential publics in social-mediated crisis communication. Literature Review Publics in Crisis Communication Publics are a “group of people who face a common issue” (Gonzelez-Herrero & Pratt, 1996, p. 84). In discussing publics and issues as core concepts in public relations, Botan and Taylor (2004) argued that a public’s interest can be objective or subjective. The interpretations of events and actions are shared by publics as a result of “a continuing process of agreeing on an
  5. 5. Informing crisis communication 5 interpretation because whether a group of people understands that it shares an interest at a particular time determines whether a public exists” (p. 655). When a public attaches importance to a certain matter, an issue occurs. The public’s interpretation of events and actions in their environment then lead to an issue they want to address. This applies to crises issues as well. Ulmer, Sellnow and Seeger (2007) referred to publics as stakeholders. Benoit and Pang (2008) referred to publics as audiences. Ulmer, Sellnow and Seeger (2007) argued that primary publics are groups of people identified by organizations as “most important to their success” (p. 37) while secondary stakeholders “do not play an active role in the day-to-day activities” (p. 37) of the organization. Fearn-Banks (2001) used the publics and stakeholders interchangeably. In discussing publics in crisis communication, Jin, Pang and Cameron (2012) adopted the approach of interchangeable definition and further proposed three characteristics the key publics in crises comprise (p. 270): 1) They are most affected by the crisis; 2) They have shared common interests, and destiny, in seeing the crisis resolved; and 3) They have long-term interests, and influences, on the organization’s reputation and operation. As Benoit and Pang (2008) argued, the identification of key publics is important because different audiences have “diverse interests, concerns, and goals” (p. 247). The task of key publics identification becomes even more critical and complex in social-mediated crisis communication. During crises, publics turn to social media for a wide variety of information and support (Macias, Hilyard, & Freimuth, 2009; Stephens & Malone, 2009). Social media play a role in both the causes and spread of crises, as the choice of channel and the consumption of social-mediated crisis content can strongly impact publics’ perceptions of organizational reputation (e.g., Schultz, Utz, & Göritz, 2011; Wigley & Fontenot, 2010). Social media can also
  6. 6. Informing crisis communication 6 facilitate crisis information sharing, opinion sharing, and emotional expression in times of crises (Macias, Hilyard, & Freimuth, 2009; Smith, 2010). The Social-Mediated Crisis Communication (SMCC) Model and Key Publics To explain how social media, traditional media, and word-of-mouth communication interact in terms of crisis information and to what extent those crisis messages influence crisis preparedness, response and recovery, the SMCC model (see Figure 1) emerged as the first theoretical model to address the need for empirical model development and testing specific to understanding crisis communication in the landscape of social media (Jin & Liu, 2010 Jin, Liu, & Austin, 2014; Liu et al., 2012). Key factors of crisis communication in the complex media landscape include characteristics of crises, organizations, publics, and communications such as information form and content. The SMCC model argues that social media should be in the media mix for crisis communication and issues management (Liu et al., 2012). It describes the relationship between an organization, key publics, social media, traditional media, and offline word-of-mouth communication before, during, and after crises. Key SMCC publics and information flow. The SMCC model identifies three key publics who seek, produce, or share information before, during, and after crises: influential social media creators, social media followers, and social media inactives (Jin, Liu, & Austin, 2014). Specifically, influential social media creators develop and post crisis information online. Social media followers consume crisis information from social media creators and also share this information both on and offline. Social media inactives do not participate actively in the social media, but receive this crisis information via other channels from social media followers, creators, or other inactives.
  7. 7. Informing crisis communication 7 As represented in Figure 1, solid arrows represent direct relationships in the flow of crisis information, created or shared by direct publics, while dotted arrows represent indirect relationships in the flow of crisis information, again, created or shared by indirect publics. These arrows, whether referring to direct or indirect relationships in the crisis information flow, also indicate a two-way, reciprocal flow of information. The SMCC highlights three main channels of crisis communication, including social media, traditional media, and offline word-of-mouth communication. The organization responding to an issue (in pre-crisis) or active crisis and the key publics are situated surrounding the organization to represent the ubiquitous nature of online and offline word-of-mouth communication among the organization and social media creators, followers, and inactives, in a given issue or crisis situation. Therefore, social media, carrying issue or crisis information from influential social media creators or the organization, has a direct relationship with key publics, the organization, and traditional media, while traditional media has a direct relationship with social media, key publics, and the organization. A Social Network Approach to the Social-Mediated Crisis Communication model The SMCC model, as discussed earlier, defines three types of publics: Influential social media creators, who develop and post crisis information online, social media followers, who consume and share information from social media creators, and social media inactives, who do not participate actively. Approaching Twitter activity as a social network, two types of actors are conceptualized: Influential social media creators and social media followers. The last public, social media inactives, cannot be examined within data created by active social media users. A social network is created when connections (“links”) are created among social actors (“nodes”), such as individuals and organizations (Wasserman & Faust, 1994). Social media platforms allow users to form connections among themselves in the process of sharing content.
  8. 8. Informing crisis communication 8 Research on social media from a social network perspective shifts the focus from individual traits to relational ties between social entities. Collections of these ties or connections aggregate into emergent patterns or network structures. On Twitter, social networks are composed of users and the connections they form with other users when they mention and reply to one another (Hansen, Shneiderman, & Smith, 2011). Given the opportunity to interact freely, social actors create sub-groups in which interconnections are more prevalent than connections with others outside that sub-group (Granovetter, 1973; Watts & Strogatz, 1998). Such sub-groups can also be seen as community structures within networks (Newman, 2004). A cluster is a sub-group of individuals who are tightly interconnected, and rather disconnected from users outside their cluster. Clusters on Twitter are composed of dense sub-groups of interconnected Twitter users that provide the channels through which users are exposed to tweets. A user’s Twitter cluster determines the tweets to which that user is exposed. For example, users may talk about an organization, topic or a product. The sources of information may be within their cluster. Users are likely to either be exposed directly (by mentioning them, for instance) or indirectly, via their cluster-mates who may re-tweet messages. A cluster on Twitter, then, determines users’ and organizations’ immediate networks. These clusters, however, are not completely disconnected from one another, but have limited connectivity. Key users are located in the network in a position that allows them to bridge clusters. One of the earliest references to these key users can be found in Milgram’s (1967) work. Milgram found that regardless of the size of a social network, human society is composed of small clusters of tightly interconnected individuals, who are connected by a few individuals strategically located in the larger network. Burt’s (1992, 2001) theory of structural
  9. 9. Informing crisis communication 9 holes identified individuals and organizations in unique positions in a network, where they connect other actors that otherwise would be much less connected, if at all. Bridges enjoy strategic benefits, such as control, access to novel information, and resource brokerage (Burt, 1992, 2001). Himelboim, Golan, Moon and Suto (2014) coined the concept “social mediators” to describe Twitter users that are both highly connected within their cluster and bridge clusters. They illustrated the role such users play in the flow of information from an organization (the U.S. State Department, in their study) to clusters of users (i.e., clusters) that they cannot reach directly. Defining SMCC Key Publics in Social Network Analysis Given the direct and indirect relationships social media have with the key publics according to different flows of crisis information and based on the definition of social mediators and clusters from social network analysis’ perspective, in this study, we further elaborate and operationalize SMCC key publics in the terms of social network analysis for consistency and congruence across concepts and measures. Since social media inactives cannot be examined via social network analysis, which applies only to data created by active social media users, we only focus on key social media creators and social media followers in this study. Social mediators are influential social media creators. Social mediators will be used, from a social network analysis perspective, to indicate key social media creators in the SMCC model. These influential social media users play a key role in creating and passing along information and are contextualized here as social mediators, earlier discussed by Himelboim et al. (2014). Social mediators are located in a key position in their network. Their content receives more attention and is shared more than other users in their cluster. They also bridge an organization with users who are not in its cluster, and therefore cannot be reached directly. In
  10. 10. Informing crisis communication 10 the context of organizational relationships with stakeholders, social mediators play a key role in connecting an organization’s official social media platforms, such as its Twitter account, with users that it cannot reach directly. Examining a Twitter conversation as a social network, social mediators can be identified as they connect organizations with their indirect social media followers. Clusters are social media followers. Clusters will be used, from a social network analysis perspective, to indicate social media followers proposed in the SMCC model. Social media users who interact with one another, share key information sources and are rather disconnected from others, form their own public. While the original SMCC definition focuses on followers, the operationalization of it as “clusters” suggests expanding the definition of connections among social media users to relationships that indicate actual attention giving and information sharing, namely mentions, retweets and replies on Twitter. Conceptualizing these SMCC publics (active social media users who are not social mediators) as clusters opens the opportunity to refine our understanding of social media followers. The network structure of these clusters (i.e., the patterns of connections) informs us about the nature of information flow among members of a public. Three structures are relevant here: 1) level of interconnectedness (density; Scott, 2012); 2) formation of a hub-and-spoke structure (Park & Thelwall, 2008); and 3) level of mutuality of relationships (reciprocity; Wasserman & Faust, 1994). Highly interconnected clusters suggest that members of a public rely on one another for sharing information, as they are more interconnected by relationships of mentions, retweets and replies. A low density suggests that either the users are sparsely interconnected or that they rely on single or very few dominant users. A hub-and-spoke structure (Park & Thelwall, 2008) is when users in a cluster are connected to a single highly connected user while
  11. 11. Informing crisis communication 11 remaining disconnected from one another. This structure reflects a public that receives its information from a single source, may or may not communicate back with that source. Regardless, social media users in this public hardly exchange information with one another. Mutuality of relationships, or reciprocity, in a cluster indicates the direction of information flow. Low reciprocity indicates one-way flow of information, from some users to others. Higher levels of reciprocity indicates an exchange of information among users (Wasserman & Faust, 1994). In sum, conceptualizing social media followers as clusters can further refine the SMCC key publics classifications. A cluster that includes an organization’s social media platform such as its Twitter account are direct social media followers. Such a cluster is composed of social users who interact directly with the organization, providing direct access to these users. Other clusters are composed of social media users who talk about the organization or an issue relevant to it, but do not include the organizational account. Such publics are therefore indirect social media followers. Both types of clusters will be further examined in this study. Research Questions This study takes a social networks approach to refine the conceptualization of two Social- Mediated Crisis Communication model publics: influential social media creators and social media followers. To examine these publics in light of the SMCC model and the social networks approach, a case study of the Twitter conversations surrounding several U.S.-based airlines is used. Influential social media creators, conceptualized here as social mediators (Himelboim et al., 2015), bridge an organization and publics it cannot reach directly. This bridging role has been traditionally reserved for news media, but now can be assumed by any user who is located in a strategic location in the network. These bridges are particularly important in crisis
  12. 12. Informing crisis communication 12 communication when quick and broad dissemination of information is paramount. The nature of these social mediators is the core of the first two research questions: RQ1: What are the industry-wide social mediators characteristics? RQ2: How, if at all, do the social mediators differ between airlines? Social media followers are conceptualized as social networks clusters. The structure of a cluster serves as an indicator of key aspects of information flow, share and exchange among users. The third research question is therefore: RQ3: How do clusters differ in terms of their network structures? Methods This study takes a social networks approach to studying the Twitter talk about 11 key U.S.-based airlines: Alaska, American Airline, Delta, Frontier, Hawaiian, JetBlue, Spirit, Sun Country, United, US Airways and Virgin America. It applies network analysis, the analysis of patterns of interactions among social actors, to identifying key social actors and publics. As previously noted, airlines were selected as the industry of interest because of their frequent need for crisis communication and their relatively well-developed social media platforms. Data Twitter usernames, user statistics (e.g., profile description and URL), and mention and reply-to relationships were collected about users who participated in the conversations about these 11 airlines. Data was collected for each airline every Tuesday for 4 weeks, from 1/20/2015 to 2/10/2015. Each search query resulted in about a week of data. Twitter Application Programming Interface (API) determines the amount of content that can be downloaded per search query. For each airline, the search query included the airline’s main Twitter handle (e.g.,
  13. 13. Informing crisis communication 13 @AmericanAir) and its main hashtag (e.g., #AmericanAir). For purposes of standardization, only the main handles and hashtags were used, as not all airlines used secondary or other affiliated accounts (e.g., @DeltaAssist). We collected data using NodeXL’s Twitter Search importer (Hansen, Shneiderman & Smith, 2011), which identifies Twitter users who included the hashtag and handles in their posted content. A total of 43 datasets were retrieved (for technical reasons, one American Airlines dataset was corrupted and could not be used). A total of 185,046 users and 503,316 messages were collected; 269,740 tweets established unique relationships of mentions and replies among users. Measurements Network analysis. The clusters in the topic-networks were identified using the Clauset- Newman-Moore algorithm (Clauset, Newman & Moore, 2004). This algorithm, as many others, typically results in a few large clusters and many very small ones. In order to identify the largest clusters in each dataset, we used a scree plot method to determine the threshold between low and high values. This approach, which originated as a method to identify key components in factor analysis, has been successfully used to categorize values as low/high, when the distribution is highly skewed (e.g., HImelboim, Gleave & Smith, 2009). A total of 213 major clusters were identified across the 43 datasets. As a public was defined earlier as a cluster (i.e., a group of interconnected users), the unit of analysis for the structural measurements here is a network cluster. The Density of each cluster was calculated as the number of existing relationships (mentions or replies) among Twitter users within a given cluster divided by the total number of possible relationships among those same users. Average geodesic distance is measured by calculating the shortest paths between all pairs of users in a given cluster and then calculating the average value. Reciprocity is calculated as the
  14. 14. Informing crisis communication 14 portion of reciprocal relationships (e.g., user A mentioned user B, and user B mentioned user A) of all existing relationships in a given cluster. NodeXL was used to calculate these structural metrics. Identifying social mediators. Each topic-network consists of nodes and directed relationships (e.g., mentions and replies). We propose here to operationalize Twitter social mediators as users who are at the top 3.5% in the entire network in terms betweenness centrality values and the top 1% in terms of in-degree centrality within each cluster for that user. This operationalization gives priority to betweenness centrality as the key aspect of mediating in bridging across clusters, while taking into consideration which of these users gained most attention (in-degree) within their own clusters. Betweenness centrality measures the extent that the actor falls on the shortest path between other pairs of actors in the network. The more people depend on an actor to make connections with other people, the higher that actor’s betweenness centrality value becomes. This value is therefore associated with bridging actors in a network, and therefore clusters. However, betweenness centrality measures do not take into consideration the direction of relationships. As director of information flow from an organization to audiences in a social network, a social mediator should not only connect, but also attract large audiences. The second aspect of the operationalization of social mediators should therefore be high in- degree centrality. In-degree centrality is measured as the number of followers a user has among the other members of the specific topic-network. Hi in-degree Twitter accounts for a significant amount of information flow through the Twitter networks due to the expected severe skew on distribution of Twitter followers (Raban & Rabin, 2007). Using this method, a total of 305 social mediators were identified.
  15. 15. Informing crisis communication 15 Classification of social mediators. We classified Twitter users into types of social actors. We iteratively developed a classification system based on preliminary analysis of all users in a single day of data collection (11 datasets on 1/20/2015). We identified five types of social actors: (1) the airline itself (e.g., @delta) or users affiliated with it (@deltaassist), (2) other airlines (e.g., @suncountryAir in a dataset about @delta and #delta), (3) Media organizations (e.g., @cnnnews), (4) Celebrities (e.g., @keeganallen), and (5) grassroots, which include individuals or small advocacy groups not affiliated with a larger institution or organization. We coded as (6) Other, Twitter accounts of airports, sports teams, and other types of organizations not included in the classification above. Intercoder reliability was sufficient (Cohen’s Kappa = 0.924). Other measurements. Size of an airline was calculated based on its number of passengers in 2014 according to the United States Department of Transportation (Virgin America’s data was not available on the website and was retrieved from the company’s website). The distribution of number of passengers across airlines was not normal, with clear high and low values, and therefore the median (33,894 passengers monthly) rather than the mean (18,992 passengers) was used to define the threshold between large (Delta, American Airlines, United, US Airways, and JetBlue) and small airlines (Alaska Airlines, Spirit, Frontier, Hawaiian Airlines, Virgin America, and Sun Country). It should be noted that only JetBlue was affected by the selection of median rather than mean as a threshold. Findings Network analysis was applied to 43 datasets of 4 weeks of airline-related activity, 185,046 users and 269,740 unique relationships of mentions and replies among users. 303 social mediators were identified, of which 21 were unrelated to the airline conversation (there were a
  16. 16. Informing crisis communication 16 few occasions where #united captured the Manchester United sports team tweets, although the team’s handle is @ManUtd and its hashtag is #mufc). The unrelated players were removed, leading to total of 282 social mediators. RQ1: What are the industry-wide social mediators characteristics? Examining the activity created by users mentioning and hashtagging top U.S. airlines’ Twitter, four types of social mediators emerged. Each accounts for about a fifth of the all mediators (N=282): individuals and grassroots organizations (n=63; 22.34%); accounts and affiliated accounts of the airline of conversation (e.g., @deltaassist in the dataset about @Delta and #Delta; n=59; 20.92%); accounts and affiliated accounts of an airline, not the topic of conversation (e.g, @united in the dataset about @Delta and #Delta; n=59; 20.92%); and celebrities (n=53; 18.79%). News media captured only 8.16% of social mediators (n=23). Other mediators (n=25; 8.87%) included Twitter accounts of airports, for and not-for-profit organizations, and sports teams affiliated with the airlines. See Figure 2. -------- Figure 2 about here -------- RQ2: How, if at all, do the social mediators differ between airlines? Types of social mediators varied across airlines. A closer examination of the data shows that the portion of social mediators affiliated with an airline, was significantly (F=12.03, p<.01) higher in the smaller airlines in terms of number of passengers (M=.51; SD=.28) than in the larger airlines (M=.13; SD=.05). In contrast, other types of users made a larger portion of social mediators in the larger airlines than in the smaller ones. Differences were significant for individuals and grassroots (F=18.34; p<.01) and news media (F=16.40; p<.01). Significance values reported here are a result of a single ANOVA test (for the entire model: F=18.34; p<.01;
  17. 17. Informing crisis communication 17 Adjusted R2 =.63). See Figure 3. For examination of social mediators’ type by airline, see Figure 4. -------- Figures 3 and 4 about here -------- RQ3: How do clusters differ in terms of their network structures? Findings suggest that direct and indirect publics exhibited different network structures. An ANOVA test was applied to examine the relationship between direct/indirect publics and the three network structures. Cluster density was found to be significantly (F=19.87; p<.001) higher in indirect public clusters (M=.0027; SD=.0032) than in direct public clusters (M=.0112, SD=.0125). Average geodesic distance was found to be significantly (F=48.88, p<.01) lower in direct public clusters (M=2.010, SD=.043) than in indirect public clusters (M=2.949, SD=.868). The value of 2 for the average geodesic distance (together with a very low SD) is a key characteristic of networks where a single key user connects most or all others (a spoke-and-hub structure). As any two users are connected via a central user, the average geodesic distance (AGD) for each pair of users is equal to 2. Findings therefore suggest that direct publics are star- shaped, where the organization account is the focal point that connects all users (Watts, 2014). In fact, examining specific social mediators types by the average AGD value of all clusters is higher than 2 (other airline: M=3.56, SD=.72; grassroots: M=2.62; SD=.80; news media: M=2.83; SD=.1.01), except for celebrity mediators (M=2.19; SD=.45). Last, the relationship between cluster reciprocity and direct/indirect clusters approached significance (p=.052). Direct publics cluster exhibited a lower reciprocity level (M=.0189, SD=.0083) than indirect public clusters (M=.0293; SD=.0339). See Figure 1 for illustration of cluster structures. Figure 5 illustrates social mediators, social media clusters, and the variety of network structures clusters may take, using the Twitter activity surrounding American Airlines
  18. 18. Informing crisis communication 18 (@americanair, #americanair). In Figure 5, circles represent users and the connecting lines are mentions and replies. Clusters are the sub-groups of interconnected users that, as illustrated, are interconnected more among one another than with users in other clusters. The airlines and social mediators are identified by their accounts’ logos (if easily recognized) or by name. The airline cluster (its directed public; top left) has the airline for which data was collected, and the most interconnected user in that cluster (here: American Airlines). Other key users emerged in this directed public cluster, such as other accounts associated with @dr_capt_ron (self-described as “Professor of Athletic Training … and Boat Captain”), @aaroncarpenter (a 15 years old avid social media user) and the Fox 4 news channel form North Texas. Another cluster, such as the “other airlines” cluster (top-center) include accounts of other airlines (such as, Delta and US Airways) that participate in the conversation about American Airlines on Twitter. The cluster at the center of the graph is around the user @paperwash, a popular Twitter user who is known for their funny tweets. On the top-right we can find the @dloesch mediators (Dana Loesch), a talk radio host. These clusters are different in terms of the social mediators who bridge the airline with its indirect publics. These clusters are also different in terms of their structure. The Airline cluster (American Airlines) has a hub-and-spoke like structure, where most users do not engage with one another. This is also the case for the @paperwash and @dloesch clusters. The Airlines cluster (top-center) as well as the cluster with Boing and Texas Airport as social mediators (bottom-left), are much more interconnected. -------- Figure 5 abut here -------- Discussion By employing a network analysis method to examine airline-related tweets this study teases out details about key publics. These details contribute to theory through elaboration of the
  19. 19. Informing crisis communication 19 SMCC model. They contribute to practice through identification and parsing of public types prior to an organizational crisis. A summary of findings and analysis follows. The SMCC model identifies three types of key publics, two of which were examined in this study. Findings provide evidence that those two publics can be elaborated. Influential social media creators (operationalized in this study as social mediators) were teased out of the Twitter networks of these airlines. They were almost equally individuals and grassroots, the airline itself, other airlines, and celebrities. Trailing was the news media as a social mediator. This finding reinforces the notion that communication channels during crises must reach well beyond traditional sources of news distribution. By identifying the social mediators in their network, organizations can more efficiently and rapidly distribute important information. Not only is it imperative for crisis communicators to know their social mediators, it is also relevant for them to know their social media clusters (i.e., publics). The findings in this study suggest that direct and indirect social media publics are, indeed, different animals. We found a different structure between direct and indirect clusters. Direct publics’ clusters were star-shaped in their presentations meaning that the organizational Twitter account is the focal point linking all users in direct c public clusters, back to the organization. Direct and indirect public clusters differ in specific ways. Indirect clusters are denser than direct clusters suggesting a broader reach of direct users clusters. Direct clusters’ Average Geodesic Distance averaged the value of two, suggesting that direct public clusters are developed around a single Twitter user (Watts, 2014). Beyond the effect of the type of Twitter cluster publics, the type of organization (i.e., size of airline) is linked to differences in social mediators and cluster publics. Small airlines’ networks are dominated by the organization’s own Twitter account. For example, Spirit and
  20. 20. Informing crisis communication 20 Frontier airlines, both identified as “small” by the parameters of this study, showed that all or nearly all their social mediators were the organization’s own Twitter feed. Large airlines showed more balance and diversity of social mediators. Based on the study’s findings there are several theoretical implications for SMCC. First, social mediators (or influential social media creators) are not an amorphous whole. In fact, they are identifiable and distinct. We propose refining the influential social media creators category in SMCC to reflect these distinctions. New categorization for influential social media creators should be: 1) individual and grassroots social media creators, 2) organizational social media creators, 3) industry social media creators, 4) celebrity social media creators and 5) journalist social media creators. Second, as with the definition of influential social media creators in SMCC, the definition of social media followers was found to be more specific. These findings should also be reflected in a revised SMCC as direct and indirect social media followers. Third, organization differences within an industry also appear to affect Twitter networks. Therefore we propose size be added to the SMCC model as an organizational variable. Beyond the theoretical implications for the SMCC model are some practical implications for crisis communicators. First, network analysis provides a methodological way in which to identify key, perhaps unanticipated, social mediators between an organization and its publics. Crisis communication plans generally include lists of publics to contact when a crisis strikes. As this research showed, news media are increasingly irrelevant in quick, crisis communication, especially via social media like Twitter. Knowing who are the influencers within your organization’s Twitter network before a crisis strikes, will allow those thought leaders to be part of your crisis communication plan. While we have identified the major social mediator
  21. 21. Informing crisis communication 21 categories for U.S.-based airlines and believe those categories will carry to other industries, we acknowledge that there may be additional categories depending on the industry. Network analysis of a Twitter feed in a non-crisis environment of other industries will identify those categories. Second, the evidence shows that social mediators and direct and indirect social media followers play a communication role for organizations via Twitter. This reinforces the dictum to build communication relationships before you need them (i.e., when a crisis hits). While the airline industry has a relatively robust social media presence, no matter the industry or organization it is advisable to consistently and proactively build social media networks via Twitter and other social media. Limitations and Future Directions As with any study, this project had limitations. Because the analysis was limited to the airline industry in the U.S. it may not be generalizable to airlines worldwide or to other industries. However, as noted in the discussion, we do feel the social mediator categories are sufficiently broad so as to be applicable for organizations beyond airlines. Nevertheless, network analysis of another industry might yield yet additional social mediator classifications. A second limitation is that without content analysis we do not know the valence of the communication. That is, we do not know whether the identified social mediators are positive or negative influencers. This is important and the topic for a future study. Beyond content analysis to determine tweet valence, this method should be employed in other crisis prone industries such as energy or food service.
  22. 22. Informing crisis communication 22 References Austin, L., Liu, B. F., & Jin, Y. (2012). How audiences seek out crisis information: Exploring the social-mediated crisis communication model. Journal of Applied Communication Research, 40, 188-207. Benoit, W. L., & Pang, A. (2008). Crisis communication and image repair discourse. In T. Hansen-Horn & B. Neff (Eds.), Public relations: From theory to practice (pp. 244- 261). Boston, MA: Pearson Allyn & Bacon. Botan, C. H., & Taylor, M. (2004). Public relations: State of the field. Journal of Communication, 54(4), 645-661. Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press. Burt, R. S. (2001). Structural holes versus network closure at social capital. In R. Burt, K. Cook, & N. Lin (Eds.) Social capital: Theory and research (pp. 31-55). New York: Aldine de Gruyter. Cameron, G. T., Pang, A., & Jin, Y. (2008). “Contingency Theory,” in T. L. Hansen-Horn & B. D. Neff (Eds.), Public Relations: From theory to practice, 134-157, New York: Pearson. Chen, Z. & Reber, B. H. (in press). “Examining public responses to social media crisis communication strategies in the United States and China,” in Y. Jin and L. L. Austin (Eds.), Social Media and Crisis Communication. New York: Routledge. Cho, S. & Cameron, G. T. (2006). Public nudity on cell phones: Managing conflict in crisis situations. Public Relations Review, 32(2), 199-201. Clauset, A., Newman, M.E.J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.
  23. 23. Informing crisis communication 23 Coombs, W. T. (2012). Ongoing crisis communication: Planning, managing, and responding (3rd ed.). Thousand Oaks, CA: Sage. Fearn-Banks, K. (2001). Crisis communication: A review of some best practices. In R. L. Heath & Gabriel Vasquez (Eds.). Handbook of Public Relations (pp. 479-485). Thousand Oaks, CA: Sage. Gonzalez-Herrero, A., & Pratt, C. B. (1996). An integrated symmetrical model for crisis- communications management. Journal of Public Relations Research, 8(2), 79-105. Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology, 1360-1380. Hansen, D.L., Shneiderman, B. & Smith, M.A. (2011) Analyzing social media networks with NodeXL: Insights from a connected world. Burlington, MA: Morgan Kaufmann. Himelboim, I., Gleave, E., & Smith, M. (2009). Discussion catalysts in online political discussions: Content importers and conversation starters. Journal of Computer Mediated Communication, 14(4), 771-789. DOI: 10.1111/j.1083-6101.2009.01470.x Himelboim, I., Golan, G.J., Suto, R.J. and Moon, B.B. (2014). A Social Networks Approach to Public Relations on Twitter: Social Mediators and Mediated Public Relations. Journal of Public Relations Research, 26(4), 359-379. DOI:10.1080/1062726X.2014.908724 Jin, Y., & Liu, B. F. (2010). The blog-mediated crisis communication model: Recommendations for responding to influential external blogs. Journal of Public Relations Research, 22, 429-455. Jin, Y., Liu, B. F., Anagondahalli, D., & Austin, L. (2013). Scale development for measuring stakeholder emotions in organizational crises. Paper presented at the International Communication Association Conference, London.
  24. 24. Informing crisis communication 24 Jin, Y., Liu, B. F., & Austin, L. L. (2014). Examining the role of social media in effective crisis management: The effects of crisis origin, information form, and source on publics’ crisis responses. Communication Research, 41, 74-94. Jin, Y., Pang, A., & Cameron, G. T. (2012). Toward a publics-driven, emotion-based conceptualization in crisis communication: Unearthing dominant emotions in multi- staged testing of the Integrated Crisis Mapping (ICM) model.” Journal of Public Relations Research, 24, 266-298. Ledingham, J. A. (2006). Relationship management: A general theory of public relations. In Botan, C. H. and Hazleton, V. (Eds) Public Relations Theory II (pp. 465-483). Lawrence Erlbaum Associates: Mahwah, NJ. Liu, B. F., Fraustino, J. D., & Jin, Y. (2015a). Jumping on the social media bandwagon?: How disaster information form, source, type, and prior disaster exposure affect public outcomes. Journal of Applied Communication Research, 43(1), 44-65. Liu, B. F., Fraustino, J. D., & Jin, Y. (2015b). Social media use during disasters: How information form and source influence intended behavioral responses. Communication Research. Published online before print January 13, 2015, doi:10.1177/0093650214565917 Liu, B. F., Jin, Y., Briones, R., & Kuch, B. (2012). “Managing turbulence in the blogosphere: Evaluating the Blog-Mediated Crisis Communication model with the American Red Cross,” Journal of Public Relations Research 24, 353-370. Macias, W., Hilyard, K., Freimuth, V. (2009). Blog functions as risk and crisis communication during Hurricane Katrina. Journal of Computer-Mediated Communication, 15(1), 1-31. Milgram, S. (1967). The small world problem. Psychology Today, 2, 60–67.
  25. 25. Informing crisis communication 25 Newman, M.E.J. (2004). Detecting community structure in networks. The European Physical Journal B, 38(2), 321–330. Park, H.W., & Thelwall, M. (2008). Developing network indicators for ideological landscapes from the political blogosphere in South Korea. Journal of Computer‐Mediated Communication, 13(4), 856-879. Scott, J. (2012). Social network analysis. Sage. Smith, B. G. (2010). Socially distributing public relations: Twitter, Haiti, and interactivity in social media. Public Relations Review, 36, 329-335. Stephens, K. K., & Malone, P. C. (2009). If the organizations won’t give us information…: The use of multiple new media for crisis technical translation and dialogue. Journal of Public Relations Research, 21, 229-239. Ulmer, R. R., Sellnow, T. L., & Seeger, M. W. (2007). Effective crisis communication. Thousand Oaks, CA: Sage. Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. New York: Cambridge University Press. Watts, D.J. & Strogatz, S.H. (1998). Collective dynamics of 'small-world' networks. Nature 393(6684), 440-442. doi:10.1038/30918 Watts, D. (2014). Social Influence in Markets and Networks (What's So Viral About 'Going Viral'?). Presented at the Rogers Award Colloquium at the University of South California, Annenberg School or Communication and Journalism. Wigley, S., & Fontenot, M. (2010). Crisis managers losing control of the message: A pilot study of the Virginia Tech shooting. Public Relations Review, 36, 187-189.
  26. 26. Informing crisis communication 26 Figure 1: Social-Mediated Crisis Communication Model (Jin, Liu, & Austin, 2014) Figure 2: Type of social mediators across airlines Social Media Followers Social Media Inactives Influential Social Media Creators Traditional Media Social Media Organization Crisis Origin• Crisis Type• Infrastructure• Message Strategy• Message Form• Social-mediated Crisis Communication Model Indirect Relationship Direct Relationship Organization Public Media Content Offline Word-of-Mouth Communication
  27. 27. Informing crisis communication 27 Figure 3: Type of social mediator type by airline size
  28. 28. Informing crisis communication 28 Figure 4: Social mediator type by airline
  29. 29. Informing crisis communication 29 Figure 5: The American Airlines social network (data collected on 1-27-15)

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