Informing_crisiscommunication_preparation_and_response_through_network_analysis - Submisison AEJ

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.

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.,

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.

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

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

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.

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.

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

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

Informing crisis communication
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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

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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

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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.,

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@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

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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.

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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

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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;

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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

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(@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

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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
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
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
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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
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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