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


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


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


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.,


@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


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.


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


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;

https://twitter.com/ManUtd

https://twitter.com/hashtag/mufc?src=hash


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


(@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


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


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


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.


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.


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.


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.


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.


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


Figure 3: Type of social mediator type by airline size


Figure 4: Social mediator type by airline


Figure 5: The American Airlines social network (data collected on 1-27-15)

informing_crisiscommunication_preparation_and_response_through_network_analysis - submisison aej

Documents