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Ferris et al: Social Media and Hurricane Evacuation International Journal of Mass Emergencies and Disasters August 2016, Vol. 34, No. 2, pp. 204-230

Studying the Usage of Social Media and Mobile Technology during Extreme Events and Their Implications for Evacuation Decisions: A Case Study of Hurricane Sandy

Thomas Ferris Erick Moreno-Centeno Justin Yates Kisuk Sung Mahmoud El-Sherif Department of Industrial and Systems Engineering Texas A&M University and David Matarrita-Cascante Department of Recreation, Park and Tourism Sciences Texas A&M University Email: [email protected]

Mobile technology, and the changing composition and purpose of social networks enabled by mobile devices have begun to noticeably impact the way self-evacuees prepare for and execute evacuations. We surveyed residents of New Jersey after Hurricane Sandy with results showing the ubiquity of mobile technology and its usage stability across social groups. During evacuation periods, increases in the use of microblogging sites were observed suggesting the importance of technology in evacuation contexts. Though traditional communication (e.g., in-person and t.v./radio) maintained high influence in decision planning, individuals exhibited a higher likelihood to accept and use information obtained through social media and mobile networks than has previously been documented. Using a k-means clustering analysis, we classified users based on their reported use of mobile technology during Sandy. Results show five distinct classification sets with varying degrees of mobile technology ownership and usage, further highlighting a changing paradigm in evacuation behaviour spurred by mobile technology. Keywords: Hurricanes, Evacuation, Social Networks, Clustering

204

Ferris et al: Social Media and Hurricane Evacuation Introduction Self-evacuees (those, according to FEMA (2009) able to leave an at-risk or impact area during an evacuation using their own transportation resources) represent the largest portion of hurricane evacuees at 80 – 85%, yet receive very limited instruction or guidance during evacuations with regard to time to evacuate, destination(s), and routes (Lindell et al. 2011). Left to their own means, self-evacuees have a large amount of autonomy in making real-time evacuation decisions, and a myriad of critical decisions (e.g. destination selection, route selection, route adaptation, refuelling, resupply, etc.) must be made in a short period of time. There are potentially devastating consequences of under-informed decisions, which can include traffic jams, running out of gas, and potentially physical harm or death. Evacuees exhibit patterns in their evacuation decision-making and behaviour which can be used to inform evacuation policy. For example, information provided by friends has greater influence than official recommendations by the local or state government (Socialmediatoday 2012). Given today’s predominance of communication among familiar parties conducted via social media and mobile technologies, it is critical that the next generation of evacuation models and analytical tools be informed by a clear definition of the role of these communication modes in emergency decision-making. Today, 4 out of 5 active internet users visit social networks and blogs. Five years ago, close to 40% of those users accessed social media content from their mobile phone (Nielsen 2011), and this number will continue to increase as mobile technologies become even more ubiquitous. Over 70% of American teens are active on social networking services, with an average of 201 people in their immediate network, and 37% of these teens send daily messages through social media outlets (Tech Media Network n.d.). Today’s teens are tomorrow’s self-evacuees, and the effective utilization of their social connections and communications via mobile technology will define the next generation of evacuation behaviour. In order to understand the roles of social media and mobile technologies in future evacuations, we must first understand how these channels are used alongside/in-place-of traditional communication modes to gather information and make decisions during extreme events. This knowledge can then inform the development of more realistic agentbased evacuation models, which can be used in governmental policymaking, prioritization of support for infrastructure resources, and strategic and tactical coordination of evacuations. While post-event studies have been conducted after nearly every recent hurricane (e.g., Baker 1991; Lindell and Prater 2007a; Kang et al. 2007), none have focused directly on the impact of mobile technology or how social media/networks are changing patterns of communication and decision-making during such extreme events. To address this knowledge gap, we surveyed residents of Monmouth County, NJ, following the events of Hurricane Sandy, one of the more massive and catastrophic storms to hit the Northeastern United States in the past half-century (CNN News US 2012). Our survey examined usage of social media and mobile 205

Ferris et al: Social Media and Hurricane Evacuation technology as well as other communication modes (face-to-face, text message, phone) used among key social groups (friends, family, co-workers) during both preevacuation and evacuation times. We also use social media, mobile device usage and demographic information from respondents to identify user groups who exhibited common pre-evacuation and evacuation behaviours. The study findings may redefine how information is conveyed to the public prior to and during extreme event evacuations. When self-evacuees are provided with realtime information regarding an emergency event and are afforded with a fast and easy means of communicating with loved ones, this can significantly affect evacuation behaviour and decision-making, which may impact the effectiveness of current evacuation management strategies. Reducing time in transit and expediting the evacuation process through real-time decision-making facilitated by social networks could lead to lower evacuation costs and higher confidence in evacuation policy. By understanding the value and limitations of these communication channels, policymaking institutions have the potential to increase individual compliance with evacuation instructions and improve the efficiency and consistency of evacuation actions and efforts. Literature Review Hurricane evacuee behaviour has been studied extensively through direct surveying of evacuees with a predominant focus on deciphering the underlying motivational considerations to evacuate. Conclusions on evacuee decision-making and the information used in making these decisions vary. However, there is agreement in a few factors that positively correlate with an individual’s decision to evacuate including perceived safety, delivery of the evacuation notification, and perceived storm severity (Baker 1991; Dow and Cutter 2000; Eisenman et al. 2007). Information pertaining to the category of the storm, the projected impact area, and the way in which information is worded/disseminated are important in determining perceived safety (Baker 1991; Eisenman et al. 2007). Additionally, factors that do not appear to affect an individual’s likelihood to evacuate include the timing of the evacuation (e.g., day or night), prior evacuation experience, and the sociodemographic factors of age, occupation, marital status and the number of children in the household (Baker 1991). Evacuees have also reported how decision-making depends to varying degrees on factors such as the reliance and importance of friends, family and peers (Baker 1991; Eisenman et al. 2007; Kang et al. 2007) and demographic factors such as disposable income, which played a major role during Hurricane Katrina (Eisenman et al. 2007).

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Ferris et al: Social Media and Hurricane Evacuation

Table 1: Review of Recent Literature Integrating Social media, Social Networks and Extreme Event Modelling

Authors

Methods

Domain

Application

(Sutton 2008)

Observation/Interview/Questionnaire/Collection of On-line Texts

DB

Wildfires

(Hughes et al. 2008)

Questionnaire/Ethnographic Work (On-site and On-line)

DB

Natural and Manmade Disasters

(Shklovski et al 2008)

Observation/Interview/Questionnaire/Collection of On-line Texts

DR

Wildfires

(Sutton 2010)

Manual On-line Keyword Search/Networked Participant Analysis/Content Analysis

DB

Hazardous Materials Disaster

(Sutton et al 2011)

Face-to-face Interviews/Phone Interviews/Thematic Content Analysis

DW

Tsunami

(Butts 2009)

Network Design/Graph Theory

NR

Misc.

DR

World Trade Center Disaster

(Butts, Petrescu-Prahova, and Cross 2007) Data Analysis/Network Modeling (Butts, Acton, and Marcum 2012a)

Time-based Network Analysis

C

(Butts et al 2012b)

Network Prediction/GIS/Graph Theory

NR

Misc.

(Lindell and Prater 2007a)

Mathematical modeling and transportation analysis

DB

N/A

(Lindell and Prater 2007b)

Decision Support/Mathematical Modeling

DB

N/A

(Lindell et al 2007)

Literature Review Paper

DB

N/A

(Widener et al 2013) Agent-based Simulation DB/DR Notes: DB = disaster behaviour, DR = disaster response, DW = disaster warning, NR = network representation, C = collaboration

207

Hurricane Katrina

Bay County, FL

Ferris et al: Social Media and Hurricane Evacuation Recent efforts have been made to better understand the role of social media, social behaviour, and social networks in evacuation modelling (Table 1). This research shows that social media and mobile technology users exhibit similar patterns of geographical on-site behaviour during extreme events (Shklovski, Palen, and Sutton 2008) and implies that the collection and assessment of event information is no longer confined by geographic bounds (Sutton 2010). This research also identifies the changing role of traditional news media (Butts 2009) and the importance of social media adoption by emergency management (Hiltz et al. 2014; Sutton, Hansard, and Hewitt 2011), including discussion on the evolving role of public information officers (Hughes and Palen 2012) as well as the policy implications arising from social media use in disaster situational awareness (CRS 2011; Grant et al. 2013). Detailed research has even been completed on specific social media networks and their influence on self-evacuee information retention and response as exemplified by (Bean et al. 2015; Tyshchuk et al. 2012; Vieweg et al. 2014). None of this research, however, has explicitly focused on how individuals have used mobile technology to actively solicit information and make decisions in extreme events from the individual’s perspective (as opposed to the emergency manager’s perspective). Knowledge of evacuee decision-making is critical to the development of robust optimization models for use in such efforts as planning humanitarian relief (Beamon and Kotleba 2006; Lodree and Taskin 2008; Ozbay and Ozguven 2007), pre-positioning (Rawls and Turnquist 2010; Salmerón and Apte 2010; Ukkusuri and Yushimito 2008), and evacuation (Franzese and Han 2002; Han et al. 2005; Sbayti and Mahmassani 2006; Sheffi et al. 1982; Pidd et al. 1996; Widener et al. 2013). Because many of these models involve parameters or variables related to decision-making, the communication modes used during the decision making process, which increasingly include social media and mobile technology, must be well-defined in order to ensure model robustness. Theoretical Model Post-event data collections (such as those in Table 1) can serve to help us describe emergent evacuation behaviour, but it is also important to look at underlying mechanisms that affect this behaviour so as to inform improved strategies for evacuation management. In the current research, we sought to determine the comparative influence of social media and mobile technologies, among other communication channels, on evacuation decisions and behaviour. But additionally, we sought to determine how these influences are expressed. To this end, Fogg’s (2009) Behaviour Model (FBM) for Persuasive Design can be consulted for understanding how the capabilities afforded by social media and mobile technologies contribute to the underlying mechanisms for the emergent evacuation behaviour. The FBM posits that behaviours are a product of three factors: motivations, abilities, and triggers. Individuals choose to act in certain ways based on the desire to do so (motivations), the capacities they have to perform such behaviour (abilities), and specific circumstances that will push them to act (triggers). Examples of motivations include 208

Ferris et al: Social Media and Hurricane Evacuation pleasure, pain, hope, fear, social acceptance, and rejection. Abilities are promoted by factors that make behaviours simple or not (simplicity factors) which include examples like time, money, physical effort, brain cycles, social deviance. Triggers can take many forms depending on the circumstances for example an alarm sound, a text message, and even a stomach growl (Fogg, 2009). The FBM states that behaviours are observed when a person has sufficient levels of motivation and ability when a triggering event occurs, and are more likely to be observed when either motivation or ability are at a high level (Fogg, 2009). A high level in one dimension can overcome a low level in the other: people with low motivation may perform a behaviour if the task if their ability to do it is high, and when motivation is high, people will engage in a specific behaviour even if it is a difficult task. However, even in the case of high motivations and abilities, without appropriate triggers a specific behaviour will not occur. Successful triggers are those that are noticeable, are associable (to the desired behaviour) and timely (i.e., the trigger happens when we are both able and motivated to perform the behaviour in question) (Fogg, 2009). Guided by the FBM, we can understand how evacuation decisions are made through the use of technology (mobile phones and social media), and how the affordances of these technologies can be (and currently are) leveraged in improving the quality of evacuation decisions. Within the disaster context, we believe motivations and abilities can be highly influenced by the use of mobile technology and social media, as motivating factors such as fear, hope, and social acceptance are heavily influenced by communications via these channels. Motivations can be supported by additional information capabilities, such as indicating evacuation routes on a mobile device interface; but they can also be diminished by the spread of false information from non-official sources, and/or the de-motivating attitudes of family, friends, and other trusted members of an individual’s cyber community. Finally, social media and mobile technologies provide a means of “triggering” the behaviour, but the threshold required to trigger behaviour depends at least partly on demographics. For example, potential evacuees who make heavier use of these mechanisms would be more likely to receive triggers through text messages, profile updates, and others forms of technology-based communication. The enhanced connectivity that the technologies provide increases the chance that a trigger will be received, and if managed appropriately (clearly linked to the evacuation need and presented at the right time), can “spark” appropriate evacuation behaviour (Fogg, 2009). Methodology Residents of Monmouth County, New Jersey were targeted for our post-event survey. Monmouth County offered a large population sample size (nearly 260,000 households in 2012 (US Census Bureau 2014)); more importantly, it was significantly affected but not devastated by Hurricane Sandy. Prior to Sandy making landfall, different regions of Monmouth were issued evacuation mandates and advisories with different degrees of severity, with mandatory evacuations primarily given to its 209

Ferris et al: Social Media and Hurricane Evacuation coastal regions (US Census Bureau 2014). Monmouth County also offered a range in median household incomes among those cities with mandatory evacuations (ranging from $39,000 (in Keansburg) to $92,000 (Old Bridge)) (US Census Bureau 2012). A systematic selection procedure was used to identify a set of households in Monmouth County that represented a range of geographic locations and socioeconomic statuses. First, a list of all mailing addresses in the county was obtained from the Monmouth County Tax Assessor’s Office. This list was then filtered to include only personal residences. The set was further divided into cities/townships within the county, and each city/township list was then sorted by household income. Finally, 1,100 households were selected such that the household incomes were uniformly distributed within each city/township and the number of selected households in each were proportionate to the populations therein. Following Dillman’s Tailored Design Method (Dillman 2000), each selected household received a postcard that was mailed on 13 April 2013 (approximately 5 months after Sandy’s landfall) announcing the upcoming survey as well as communicating information about the study. One week later, the first wave of surveys were mailed, along with a cover letter and a token gift of $2. After mailing the first wave of surveys and allowing time for delivery (approximately two weeks), a reminder/thank you postcard was sent. Two weeks after the postcard was sent, another copy of the survey was mailed to all non-responders. The second wave of mailings was followed by a new thank you letter. Finally, this process was repeated a third time for all non-responders. The survey instrument consisted of ten major questions (many of which had multiple sub-questions within) distributed in four sections. The first section of the survey was titled “Mobile Device and Social Media Usage” collected demographic information for household members, and information about the number and types of mobile devices used by each household member during their daily lives. This section also collected ratings for each individual’s frequency of use of four major social networking services: Facebook, Twitter, Google+, and Instagram. Within the FBM framework, this section served to establish the survey population’s “abilities” with regard to communicating via these channels. The second section of the survey entitled “Household Communication” collected information about individual household members’ communication trends. In this question, the head of the household was asked to rate the frequency of use for various communication modes (face-to-face conversations, phone conversations, text messaging, e-mail, and social networking) when interacting with immediate family members, other (extended) family members, friends, and co-workers/acquaintances during normal days and also during Sandy. We note that text messaging and social networking represent two different and independent modes of communication, with social networking implying the connection of an individual to a larger group of social media users on a given network (e.g., Twitter, Facebook) and where content may be disseminated without direct inclusion or action on the part of the user. In other words, text messaging requires intentional information dissemination (knowledge of the recipient’s phone number) while social networking allows for passive information dissemination (through ‘Likes’ or ‘Retweets’ that broadcast to the entire social 210

Ferris et al: Social Media and Hurricane Evacuation network of an individual). In FBM, this section most closely informed the likelihood of communication “triggers” that came from different social groups. The third section entitled “Information-Related Factors that Influenced whether and when to Evacuate” collected information about evacuation-related behaviours. Questions included evacuation in previous events, effects experienced during previous evacuations, evacuation during Sandy, distance and time for evacuation during Sandy, and a number of other factors identified in previous research as being impactful to evacuation decisions. The questions in this section helped to identify the types of information that influenced these decisions (the “motivation” factor in FBM), as well as the channels consulted to receive this information during evacuation (which further informs the population “abilities”). The final section entitled “About You” collected additional demographic information on education, employment status, and income. Additionally, space and directions were provided in this section for respondents to share any additional comments that they believed were pertinent. Data Collection In total, 177 surveys were completed and returned, yielding a response rate of 16.1%. Two survey responses were discarded due to sparse or incomprehensible information. As a result, response data were compiled for 175 Head of Household (HH) respondents and a total of 417 household residents of Monmouth County (Table 2). The average age for HH respondents was 57.12 years while the average age of all individuals with responses was 45.25 years. Gender for all individuals was reported as 51% female (compared to 51.3% of all Monmouth Country residents (US Census Bureau 2012)). The average education level of the HH was a 4-year college degree (40.5% of all Monmouth County residents 25+ report education as Bachelor’s or higher (US Census Bureau 2012). Concurrent with official county records, most were currently employed (8% of respondents were unemployed, compared to 7.1% reported unemployment for the County (Federal Reserve Bank of St. Louis 2012)), with fairly evenly distributed household incomes (median household income in Monmouth County is reported at $84,746 (US Census Bureau 2012)). When asked whether they had evacuated their household for a previous hurricane, 15.8% of respondents replied ‘yes’ and 25.4% of respondents reported experiencing the negative effects (i.e., wind damage, flooding, etc.) of a previous major hurricane. In total, 36 households (20.5% of the respondents) evacuated during Sandy. 75% of these evacuated less than 10 miles from their home and experienced total evacuation times less than 1 hour. Results Figure 1 summarizes information regarding the availability and use of mobile technologies, as well as social media usage, during normal (i.e., everyday) conditions. The surveyed population showed high ownership rates of mobile devices: 95.5% of 211

Ferris et al: Social Media and Hurricane Evacuation households owned a smartphone or cellphone, over 68% owned a laptop or iPad/tablet, and 100% reported owning at least one mobile technology. The right side of the figure illustrates high usage of the social networking service Facebook and relatively low usage of microblog sites such as Twitter and Instagram. A high usage reported for Google+ (surpassing Facebook) suggested that not all survey respondents were able to clearly differentiate the Google+ social networking services (e.g., Google Hangout) from the Google search engine, and thus these data were removed from this analysis.

Table 2: Demographic Data As Indicated By the Head of Household for Monmouth County, NJ Respondents

Income

n = 129

< $15,000 $15,000 – 24,999 $25,000 - $34,999

2

Employment

n = 100

Education

n = 145

By Company For Pay

68

High School or GED

33

7

Self-Employed

14

2 Year Technical

21

$35,000 - $49,000

13

$50,000 - $74,999

22

Unemployed but Looking

7

4 Year College (BA / BS)

60

$75,000 - $99,999 $100,000 $149,999 $150,000 +

21 29

Unemployed and Not Looking

1

Advanced Degree (MS, PhD, JD, MD)

31

28

Homemaker

10

None of the Above

0

Other

46

7

Figure 1: Mobile Device Ownership and Social Media Usage for All Household Members as Indicated By the Head of Household Respondent.

HH were asked to identify the degree to which social media and other communication modes were used to communicate within different social relationship groups during normal conditions and during Sandy. Respondents reported their usage 212

Ferris et al: Social Media and Hurricane Evacuation of each communication mode (Face-to-Face conversation, Phone conversation, Text Messaging, E-mail and Social Network) for each relationship group (Immediate Family, Other Family, Friends and Co-Workers) according to the following 5-point scale: ‘N’ = no use, ‘1’ = some use, ‘2’ = daily use, ‘3’ = hourly use and ‘U’ = unknown use. Responses were then converted to an overall “Usage” score for a specific social medium or mobile device by aggregating the numeric value reported for each household member (‘N’ was scored as ‘0’ in calculating Usage, and ‘U’ data points were not considered in this aggregation). Table 3 gives the corresponding Usage for each mode/relationship combination during normal and hurricane conditions and illustrates the difference in Usage between the two periods for each mode/relationship.

Table 3: Usage Scores by Communication Mode and Relationship

Note: ‘To Evacuate’ signifies the total usage score for the corresponding communication mode under evacuation conditions.

With the exception of Text Messaging, communication modes experienced an overall drop in Usage during the Hurricane. This is not necessarily surprising as the impending approach of Sandy would require increasing amounts of action and decreasing amounts of communication as sheltering-in-place or evacuation activities became more imminent. Additionally, Sandy had a significant and well-documented impact on physical infrastructure, including cell and electricity towers, and the resultant inability for service providers to maintain reliable service during the event likely influenced the lower Usage of all but the simplest forms of electronic communication. 213

Ferris et al: Social Media and Hurricane Evacuation The reported Usage, and specifically the difference between normal and hurricane conditions, suggests some interesting insights into the changing influence of mobile technology and social media use in extreme events. First, communication via Text Messaging seems to show the least amount of change in Usage across all social groups between normal and hurricane conditions, and possibly shows an increase in Usage during hurricane conditions for groups outside of the Immediate Family. Second, communication via social networking showed comparatively smaller decreases in Usage among Friends and Co-Workers than Immediate and Other Family members, while other forms of communication showed relatively consistent or larger Usage decreases for non-family social groups. Third, Face-to-Face communication decreases to a larger extent as the social relationship is farther from the individual, which seems to oppose the pattern with Social Network Usage. These findings suggest that as one moves outside of immediate family social groups, text messaging and social networking are go-to modes of communication during hurricane conditions, and show the role of these modes in an individual’s ‘sphere-of-influence’ during such extreme events. A repeated measures Analysis of Variance (ANOVA) was used to further explore the effects of Usage data obtained from each HH to determine the effects of communication mode and condition (Normal vs. Hurricane), and the interaction of these two factors, for each social group. Table 4 illustrates key findings of this analysis and can be used to see when Usage changed significantly between normal and hurricane conditions, as well as Usages that were similar across modes in each condition. Table 4 shows 5 statistically dissimilar groupings (denoted by the number of columns) for “Immediate Family” and “Co-Workers”, and for “Other Family” and “Friends”. The shaded blocks within a given column indicate similar communication modes. For example, under “Immediate Family” the shading of the top three cells in Column 1 indicates that Face-to-Face under Normal conditions, Face-to-Face under Hurricane conditions and Phone under Normal conditions share a statistically similar Usage pattern. This tells us that face-to-face communication didn’t change between normal and hurricane conditions, and also that phone conversations were as frequent as face-to-face in normal conditions. Similarly, Column 5 shows that E-mail Usage during the Hurricane was statistically similar to Social Network Usage during the Hurricane and under normal conditions when contacting Immediate Family. The pvalues at the bottom of Table 4 are used to test the hypothesis that all columns under a given relationship level are statistically similar. Table 4 shows that, for every relationship group, communication via Social Networking is generally statistically similar regardless of condition (Normal versus Hurricane). Additionally, social network communication behaviour is never grouped with Face-to-Face, Phone or Text Messaging, signifying significant Usage differences between these modes under both normal and hurricane conditions. Examining across relationship levels, communication behaviour exhibited within Other Family and Friends is largely consistent. Communication with Immediate Family tends to favour ‘real-time’ and more personal communication modes, as evidenced by the separate column grouping for Face-to-Face and phone while the communication modes 214

Ferris et al: Social Media and Hurricane Evacuation selected within the Co-Workers relationship depends more on conditions (normal versus hurricane) than on the communication mode (groupings for Co-Workers typically contain at least three communication modes).

Table 4: Usage Groupings by Mode, Condition and Relationship

Note: In each column, cells that share the same color shading represent groupings for which usage scores did not significantly differ.

To further examine communication tendencies during Sandy, additional analyses were conducted that focused only on those households that actually evacuated at some point prior to the hurricane’s landfall (37 HHs – 20.3% – reported evacuating). Two key factors affecting communication during the evacuation are the number of vehicles taken during evacuation (e.g., whether or not immediate family members are colocated in a single vehicle) and the number of mobile devices taken, which relates to communication capabilities. Table 5 provides statistics on the number (and percentage) of vehicles and smartphones taken with evacuees.

Table 5: Percentage of All Owned Assets Taken During Evacuation

For those who took along at least one mobile device during evacuation, data were gathered on social media usage during the evacuation itself. Figure 2 summarizes evacuation Usage (calculated in the same manner as in Table 3), reports an average 215

Ferris et al: Social Media and Hurricane Evacuation Usage per individual (calculated as [Usage] / [Total Number of Responders]; this is the italicized value in the figure) and highlights the change in average Usage per individual between normal conditions and during the evacuation (Percent Change).

Figure 2: Social network Usage and Average Score During Hurricane Sandy, and Usage Comparisons Between Normal and Evacuation Conditions. Numbers in parenthesis represent the aggregate Usage score divided by the total number of household users for which data was supplied.

The tendency for households to possess mobile technology and access it during an evacuation represents a significant paradigm shift from historical observations of hurricane evacuees. 92% of households evacuated with at least one smartphone and/or other devices with internet connective capability, with 57% of households taking more than one smartphone/device with them. As illustrated in Figure 2, not only were mobile devices taken during the evacuation and thus could provide access to multiple forms of communication, but they were utilized to communicate in-transit via social networking channels. Of the four social media channels identified in this survey, Usage per person (parenthesized in Figure 2) increases only for the two microblogging sites (Twitter and Instagram). Finally, information and communication aspects of the evacuation decisionmaking process were analysed. Figure 3 details the different information sources consulted and the forms of communication used in deciding whether the household would evacuate. The findings regarding information source impact are consistent 216

Ferris et al: Social Media and Hurricane Evacuation with historical behavioural decision-making, specifically in the weak impact associated with printed periodicals and the strong impact associated with observed hazards, personally delivered information (face-to-face and phone) and tv/radio (Lindell et al. 2007). We also note emerging trends, such as the growing impact of internet news sources. Another interesting finding that concurs with the communication mode Usage profile during Sandy is the proclivity of text messaging used specifically in gathering information for evacuation decision-making (used just slightly less than the more traditional face-to-face and phone communication modes).

Figure 3: Communication Modes Used and the Impact of Information Sources on Evacuation Decision Making for Self-Evacuees during Hurricane Sandy, as Reported By the Head of Household.

Interestingly, while Social Networking services played an important role in communications across all affected household members during hurricane conditions, at least specifically within heads of household and specifically in the task of deciding whether or not to evacuate, Social Networking played a reduced role. Table 6 217

Ferris et al: Social Media and Hurricane Evacuation provides more detailed information regarding how the HH used social media programs during Sandy, with tabulated Likert-style responses to the listed questions and an “Agreement” score calculated as the sum of ratings using the following quantification: strongly disagree = -2, disagree = -1, neutral = 0, agree = 1, and strongly agree = 2. It appears that Social Networking was not used more often during Sandy than it was in normal situations for most HHs, however it is unclear how often a response of “disagree that I used social media more” relates to a decrease in social media usage or a lack of change in usage. Across the population of HHs in this study, general Agreement scores suggest that Social Networking is likely more useful for HHs in coordinating and communicating among key social groups than as an official channel for broadcasting evacuation information. However, the wide distribution in responses suggests a need for deeper analysis within subgroups of respondents.

Table 6: Reporting on Social Media Use by Household during Hurricane Sandy.

During Hurricane Sandy, you used social media programs: …more frequently than I/they/we would in normal situations …to gather information about the status of the hurricane and evacuation …to gather information about the status/activities of others …to send messages to or communicate with others

SA

Agreement Score

SD

D

N

A

62

17

19

26

32

-50

53

10

21

28

42

-2

51

6

23

31

45

11

53

5

22

22

55

18

Notes: SD = Strongly disagree, D = Disagree, N = Neutral, A = Agree, SA = Strongly Agree, Statements were answered on a 5-point scale from Strongly Disagree (left) to Strongly Agree (right). Agreement scores were calculated as a weighted sum of responses.

Though it was difficult to use demographic variables collected in this survey to delineate them, there were clearly groups of respondents that showed increased usage of mobile technology in a broad sense (i.e., text messages, Social Networking, and Email) and a high likelihood to accept information throughout social media for their evacuation decisions. The next section details a method used to categorize respondents into these sub-groups and analyse their tendencies more deeply. Quantitative Extensions: Patterns and Clusters To gain deeper insight into the large degree of variance in social media and mobile technology usage across the surveyed population, we applied a K-means clustering method (MacQueen 1967) to identify meaningful subgroups of respondents. The K-means algorithm is easy to interpret and time-efficient, making 218

Ferris et al: Social Media and Hurricane Evacuation K-means the most popular clustering method in scientific and industrial applications (Berkhin 2006). Given a set of observations {𝑥_1, 𝑥_2, … , 𝑥_𝑛}, where each observation has p dimensions, the K-means algorithm aims to partition the n observations into K clusters that minimize the sum of dissimilarities between the observations in the same cluster. The optimization problem that K-means aims to solve can be written as: 𝐾

(𝑥𝑘 ) ∥2 , min ∑ ∑ ∥ 𝑥𝑖 − ̅̅̅̅̅̅

{𝑐1 ,…,𝑐𝐾 }

(1)

𝑘=1 𝑥𝑖 ∈𝑐𝑘

In (1), c_k is the set of observations assigned to cluster k and (x_k ) ̅ is the mean of observations in c_k. The optimization problem (1) is very hard to solve exactly (in technical terms, the problem is NP-hard), so the K-means algorithm is an heuristic that aims to find a good solution by iterating between two operations: 1) assigning each observation to the closest cluster center, and 2) recalculating the cluster centers based on its assigned observations. When using K-means, the user is required to prespecify the number of clusters, K. Such pre-specification may lead to poor results if an inappropriate number of clusters is pre-specified. Thus, to find an appropriate number of clusters for our collected data, we used a trial-and-error process that varied K from two to six (since our data consists of 175 respondents, we limited the upper bound of K to six so that the average size of each cluster would likely be more than 30, which is a size we deemed large enough to represent the population group). We concluded that having five clusters was appropriate since the objective function (1) decreased significantly when the number of clusters varied from two to five, whereas the objective function decreased only slightly when varying number of clusters from five to six. In our K-means clustering, instead of using each and every of the survey questions as a dimension in the analysis, we characterized each respondent using only his/her responses to the questions concerning the information on which communication modes does the HH use to communicate in normal conditions and during Sandy. We use only these questions in our input data since we believe these questions are the most closely related to the degree of likelihood that survey respondents use these communication modes for seeking credible emergency information and for making evacuation decisions. Table 7 shows the five clusters obtained with K-means. Note that there are distinct characteristic differences between the clusters. Cluster 1 contains respondents that apparently prefer (inferred from Usage tendencies) phone and text messaging over other social media programs. Cluster 2 contains respondents who prefer SNS over other communication modes for each relationship type (i.e., immediate family, other family, friends, and co-workers), evidenced by the fact that they have the highest or the second highest usage of SNS both in normal conditions and during Hurricane. Cluster 3 has the highest average age (65.1) and contains respondents with consistently low Usage regardless of the communication mode or relationship type. 219

Ferris et al: Social Media and Hurricane Evacuation Cluster 4 contains respondents who are very active in every type of communication mode. Cluster 5contains respondents with low communication Usage but differs from cluster 3 as the Usage scores of respondents in cluster 5 are typically higher than those in cluster 3 but are consistently lower than the Usage of the entire group of respondents. Table 7 summarizes the Usage, and Table 8 summarizes the basic demographics and mobile device ownership of each cluster. Table 9 shows the communication modes utilized for each cluster. Here, we observe cluster 1 respondents’ preference of text messages (1.61, the second highest value of this cluster) and cluster 2 respondents’ high usage of text messaging and SNS (2.18 and 1.77, the first and the third highest values of this cluster). The respondents in cluster 3 rarely use text messaging and SNS (0.14 and 0.05) and exhibit low overall communication Usage (under 1.0 for all communication modes). The respondents in cluster 4 show high Usage of all types of communication modes while those in cluster 5 show the second lowest Usage of all communication modes except social networks (recall that the respondents in cluster 3 have the lowest overall communication Usage). Table 10 assesses how the five clusters evaluate their social media use leading up to Sandy’s landfall. We observe that respondents in cluster 2 and cluster 4 reported more frequent social media program use during Sandy than under normal conditions (in Table 10, ‘0’ may be interpreted as a neutral response, ‘1’ as agreement and ‘2’ as strong agreement). By using K-means clustering, we can extend the observations in the previous section to help support inferences on the degree to which social networks and mobile technology enhance the dissemination of information during evacuations. We can also identify clusters that are likely to exhibit higher willingness to use mobile technology as a timely, personal and credible source of evacuation information and what their preferred format/form-of-communication is. Such inferences are important in establishing effective emergency management communication practices. High ownership rates and frequent use/consultation of information sources are critical indicators of successful social media communication during evacuation scenarios. We now examine each of the clusters in more detail as their observed behaviour pertains to this topic. By using K-means clustering, we can extend the observations in the previous section to help support inferences on the degree to which social networks and mobile technology enhance the dissemination of information during evacuations. We can also identify clusters that are likely to exhibit higher willingness to use mobile technology as a timely, personal and credible source of evacuation information and what their preferred format/form-of-communication is. Such inferences are important in establishing effective emergency management communication practices. High ownership rates and frequent use/consultation of information sources are critical indicators of successful social media communication during evacuation scenarios. We now examine each of the clusters in more detail as their observed behaviour pertains to this topic.

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Table 7: Household Clustering by Communication Usage Patterns Under Normal and Hurricane Conditions.

Cluster 1

Immediate Family

Other Family

Friends

Co-worker

Face-to-Face Phone Text Message E-mail Social Network Face-to-Face Phone Text Message E-mail Social Network Face-to-Face Phone Text Message E-mail Social Network Face-to-Face Phone Text Message E-mail Social Network

NC 2.00 2.06 1.90 1.38 0.27 1.40 1.86 1.85 1.41 0.15 1.28 1.52 1.52 1.31 0.08 1.57 1.44 1.33 1.44 0.13

HC 2.06 2.52 2.38 1.00 0.00 1.32 2.32 2.23 1.00 0.00 1.20 1.74 1.62 1.08 0.00 1.05 1.42 1.52 1.14 0.00

Cluster 2 NC 2.09 1.82 1.95 1.10 1.59 1.09 1.43 1.67 0.85 1.70 1.05 1.18 1.57 0.85 1.67 1.36 0.90 1.00 0.80 1.18

Cluster 3

HC 2.39 1.86 2.38 1.05 2.00 1.55 1.68 2.19 0.85 2.00 1.18 1.45 2.05 0.90 2.10 1.14 1.10 1.57 0.85 1.76

NC 1.54 1.31 0.33 0.66 0.15 0.89 1.04 0.23 0.67 0.21 1.10 1.04 0.26 0.48 0.17 0.73 0.74 0.12 0.46 0.08

HC 1.66 1.17 0.19 0.29 0.10 0.79 0.93 0.10 0.28 0.08 0.95 0.79 0.15 0.21 0.05 0.40 0.35 0.05 0.11 0.00

Cluster 4 NC 2.41 2.27 2.16 1.75 1.67 2.15 2.10 2.47 2.00 2.05 2.18 2.00 2.58 1.86 2.06 2.00 0.94 2.07 2.08 1.56

HC 2.25 2.10 2.17 1.35 1.39 2.06 2.20 2.65 1.83 2.19 2.06 1.88 2.62 1.93 2.27 1.40 1.88 2.33 1.46 1.27

Cluster 5 NC 1.82 1.73 1.79 1.11 0.74 1.05 1.28 1.08 0.85 0.56 1.17 1.11 1.34 1.03 0.61 1.53 1.03 1.06 1.22 0.38

HC 1.77 1.26 1.38 0.19 0.08 0.56 0.91 1.03 0.35 0.12 0.44 0.68 1.09 0.25 0.22 0.48 0.48 0.73 0.34 0.06

Overall Population NC 1.88 1.75 1.45 1.10 0.70 1.22 1.45 1.25 1.05 0.76 1.27 1.28 1.18 0.97 0.72 1.30 1.10 0.91 1.05 0.52

HC 1.95 1.67 1.47 0.64 0.51 1.10 1.47 1.36 0.72 0.62 1.04 1.17 1.21 0.68 0.67 0.76 0.88 0.98 0.61 0.46

Notes: NC = Normal Conditions, HC = Hurricane Conditions. Among the 5-point scale ('N' = no use, 'l' = some use, '2' = daily use, '3' = hourly use, and 'U'= unknown), 'N' was aggregated as 0 and 'U' was aggregated as null value when calculating the mean value. Clusters represent groupings of similar Usage patterns where households within a cluster are more similar to each other than to any household outside the cluster.

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Table 8: Cluster Size, Demographic and Ownership Descriptive as Compared to the Overall Population

Cluster 1

Cluster2

Cluster3

Cluster 4

Cluster5

Overall Population

32

23

55

25

40

175

55.9

50.5

65.l

53.3

55.6

57.4

Percentage of Males

81.3%

27.3%

52.7%

39.1%

38.5%

49.7%

Smartphone Ownership

59.4%

86.4%

20.0%

56.0%

56.4%

49. l%

Cellphone Ownership

48.4%

21.7%

69.1%

50.0%

53.8%

52.9%

iPad/Tablet Ownership

28.l %

43.5%

14.5%

36.0%

34.2%

28.3%

Laptop Ownership

68.8%

69.6%

38.2%

68.0%

53.8%

55.7%

Cluster Size Age

Notes: The number in the age category denotes the mean age value of the respondents in each cluster. The number in the percentage of ales category denotes the percentage of males in each cluster. The number in each mobile device is the ownership rate (possession ratio) of the corresponding device in each cluster.

Table 9: Communication Modes Used by Household Clusters in Deciding Whether to Evacuate Prior to Hurricane Sandy.

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Overall Population

Face-to-Face

1.44

1.65

0.89

2.08

1.28

1.38

Phone

1.78

1.96

0.93

2.32

1.32

1.53

TextMessage

l.61

2.18

0.1.4

1.95

1.18

1.24

E-mail

0.94

0.95

0.17

1.00

0.45

0.62

SocialNetwork

0.28

1.77

0.05

1.52

0.45

0.64

Notes: Among the 5-point scale('N'= no use,' l'=some use, '2' = daily use,'3'= hourly use, and 'U'= unknown), 'N' was aggregated as 0 and 'U' was aggregated as null value in calculating mean value. Modes are represented by Usage score with higher scores indicating higher use in the decision-making process.

Cluster 1 respondents’ average smartphone ownership rate (59.4%) is above the average (49.1%) and the cluster used text messaging a lot during Sandy (2.38, 2.23, 1.62, 1.52 of text message Usage respectively for immediate family, other family, friends and co-workers). Frequent use of text messaging can also be observed in the communication modes used when deciding whether or not to evacuate (Table 10). The average usage score of text messaging is 1.61, the second highest valued communication mode for this cluster. However, the usage of social media during the emergency conditions is low with cluster 1 respondents generally disagreeing (-0.60)

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with the statement that they used social media more often during Sandy than under normal conditions. We may conclude that the respondents in cluster 1 prefer text messaging as a mobile evacuation decision tool during emergency conditions, but is not very tech-savvy overall.

Table 10: Characteristics of Social Media Program Usage Pattern by Each Cluster.

More frequently than would in normal situations To gather information about the status of the Hurricane and any evacuation requirements To gather information about the status/activities of others you care about To send messages to or communicate with others who you care about

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Overall Population

-0.60

1.05

-0.93

0.71

-0.79

-0.33

-0. 17

l.19

-0.57

1.04

-0.62

-0.03

-0.10

l.43

-0.67

1.13

-0.40

0.05

0.20

1.45

-0.53

1.08

-0.53

0.09

The respondents in cluster 2 exhibited the highest smartphone ownership rate, 86.4%, among clusters and showed high text messaging and social media communication Usage during Sandy (Text Messaging: 2.38, 2.19, 2.05, 1.57 and Social Media: 2.00, 2.00, 2.10, 1.76 respectively for immediate family, other family, friends, co-workers). The frequent use of text messaging and social media can also be observed in the communication modes used when deciding whether or not to evacuate (Table 10). The average Usage scores of text messaging and social media are 2.18 and 1.77 respectively, the first and third highest values for this cluster. Frequent usage of social media also be supported through Table 11, where the respondents in cluster 2 generally agree (+1.05) with the statement that they used social media more often during Sandy than under normal conditions. We may conclude that the respondents in cluster 2 are comfortable using mobile technology (either text messaging or SNS) during emergency conditions to gather information and make evacuation decisions. Respondents in cluster 3 demonstrate a low preference for mobile technology, with the lowest smartphone, tablet, and laptop ownership rates (20.0%, 14.5%, and 38.2%), indicating a significantly lower capability to connect to the internet or social media than the other clusters. This lack of capability leads to the cluster’s extremely low adoption of text messaging and social media during Sandy (Text Messaging: 0.19, 0.10, 0.15, 0.05 and Social Media: 0.10, 0.08, 0.05, 0.00 respectively for immediate family, other family, friends, co-workers). Not surprisingly, respondents in

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cluster 3 also disagreed (-0. 93) with the statement that they used social media more often during Sandy than under normal conditions. We may conclude that respondents in cluster 3 are not likely to use mobile technology during an evacuation scenario and therefore they would not be good candidates to target with information disseminated through social media. Respondents in cluster 4 are characterized by higher-than-average smartphone ownership rates (56% compared to 49.1% for the overall population) as well as high text messaging, social media, and e-mail communication during Sandy (Text Messaging: 2.17, 2.65, 2.62, 2.33, Social Media: 1.39, 2.19, 2.27, 1.27 and E-mail: 1.35, 1.83, 1.93, 1.46 respectively for immediate family, other family, friends, coworkers). The exhibited high usage of social media during Sandy conditions is also observed by the cluster’s agreement (0.71) with the statement that they used social media more often during Sandy than under normal conditions. We may conclude that respondents in cluster 4 are willing to solicit information via mobile technology and that they are willing to use multiple technology-enabled means to do so. Lastly, respondents in cluster 5 are a somewhat contradictory group. Their average smartphone ownership rate (56.4%) is higher than the overall population average (49.1%) however, they do not communicate as consistently or clearly as other clusters (Text Messaging: 1.38, 1.03, 1.09, 0.73 and Social Media: 0.08, 0.12, 0.22, 0.06 respectively for immediate family, other family, friends, co-workers). When asked if they used social media during Sandy more often than they would in normal situations, cluster 5 respondents generally disagreed (-0.79). We may conclude that respondents in cluster 5 have the capability to access information from mobile technology but are less likely to do so than other clusters with similar capability. Conclusions This paper discussed the findings from a mail survey sent to a representative sample of residents of Monmouth County, NJ to determine how mobile technologies and social media and other communication modes were used to gather information and guide household planning and evacuation during Sandy. We identify changing patterns in communication behaviour prior to and during Sandy spurred by the ubiquity of mobile technology and social networks. We found that mobile technology is now a vital part of hurricane preparation, planning and evacuation with the vast majority of households having multiple mobile devices accessible and/or in-use through these phases. We observe Usage increases in certain types of social media (micro-blogs like Twitter and Instagram) and show a consistent degradation of more traditional (face-to-face and phone) communication methods, especially among less familiar social groups. By analysing Usage scores derived from survey results, we observe a subtle decline (as opposed to a heavy decline) in social media use during Sandy among all social groups, a characteristic that no other communication mode possessed.

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A K-means cluster analysis allowed us to classify individual respondents into one of five clusters, each with its own characteristics. We identified cluster 2 and cluster 4 that showed high tendencies to use mobile technology during the Sandy as well as cluster 1 and cluster 5 that exhibited the occasional use of text messaging during Sandy but very little social media use. The respondents in cluster 3 did not appear to rely on social media at all, preferring more traditional forms of communication. By returning to Fogg’s Behavioral Model key elements of motivation, ability, and triggers in influencing a decision (Fogg 2009), one can note specifically how social media and mobile technology are changing these elements in regard to evacuation behaviour. In terms of motivation, additional social pressures come into play and affect decisions about safety for responsible parties when they are discussed in a public forum such as those offered by social media. These pressures can have a positive (e.g., encouraging evacuation) or negative impact (e.g., [X] is not leaving, so why should I?). With regard to ability, many barriers are lowered by the ease of connecting with members of one’s social circle, as well as official sources, to obtain impactful information. These technologies are approaching ubiquity in modern society, which continues to increase the ability of evacuation decision makers to receive impactful (and motivating) information, but depend on infrastructure such as telecommunications and power grids to support this ability. Finally, the prevalence of these technologies increase the likelihood that they can be a source of “triggers” in initiating evacuation behaviour, but this behaviour will be in line with motivations. Taken together, FBM theory shows how social media and mobile technologies can improve the likelihood of observing evacuation behaviour, but illustrates the need to focus efforts on instilling the proper motivation. To this end, other existing theory that specifically addresses decision making in emergency contexts (e.g., Lindell and Perry 2012) may be consulted to determine impactful ways to motivate potential evacuees. There are a number of limitations of this study which should be taken into consideration when interpreting the results. First, despite following the Tailored Design Method (Dillman 2000) to maximize response rate, only 175 of 1100 surveys (16%) were returned with useable data (on 417 individuals). Although our demographic data are roughly in line with official records for Monmouth County, ideally the sample size, and thus resulting dataset, would be larger. Another limitation stems from the fact that heads of household were specifically instructed to fill in response data for all occupants of the house. The heads were chosen 1) to reduce the likelihood of multiple survey responses from the same house, and 2) because they were deemed most likely to be involved in evacuation decisions. However, household heads may not be the most knowledgeable about technology usage patterns nor the most influential information sources for every member of the household. Perhaps future efforts may seek more direct input from those who are more active in interactions with these technologies and with members of a social network. We conclude by offering the following remarks. First, effective communication by emergency managers will not be solved by a single focus or strategy, such as a focus on increasing information dissemination through social media. Strategies that focus only on one communication mode will clearly not be effective for all

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individuals. Second, the observations stemming from our survey point towards a changing trend in communication behaviour during Sandy. The next generation of evacuees is utilizing social media and mobile technology more holistically and those preferences under normal conditions are impacting how these individuals communicate during extreme events. Considering the increasing ownership rate of mobile devices and the high levels of social media activity in younger generations, we can expect that mobile technology will be a more relied upon source to provide timely, personal, and credible evacuation information in the near future. References “Assessment Records Search, Monmouth County, NJ 2012,” 2002. NJ Assessment Records Search, Accessed on 5/7/2015. (http://tax1.co.monmouth.nj.us/cgibin/prc6.cgi?menu=index&ms_user=glou&passwd=data&district=0801&mode=1 1) Baker, Earl J. 1991. "Hurricane Evacuation Behavior." International Journal of Mass Emergencies and Disasters 9 (2): 287-310. Beamon, Benita M., and Stephen A. Kotleba. 2006. "Inventory Modelling for Complex Emergencies in Humanitarian Relief Operations.” International Journal of Logistics: Research and Applications 9 (1): 1-18. Bean, H., J. Sutton, B.F. Liu, S. Madden, M.M. Wood, D.S. Mileti. 2015. "The Study of Mobile Public Warning Messages: A Research Review and Agenda." Review of Communication 15 (1): 60-80. Berkhin, Pavel. 2006. "A Survey of Clustering Data Mining Techniques." Pp. 25-71 in Grouping Multidimensional Data. Berlin: Springer Berlin Heidelberg. Butts, Carter T. 2009. "Revisiting The Foundations of Network Analysis." Science 325 (5939): 414-416. Butts, Carter T., Miruna Petrescu-Prahova, and B. Remy Cross. 2007. "Responder Communication Networks in the World Trade Center Disaster: Implications for Modeling of Communication Within Emergency Settings." Mathematical Sociology 31 (2): 121-147. Butts, Carter T., Ryan M. Acton, and Christopher Steven Marcum. 2012a. "Interorganizational Collaboration in the Hurricane Katrina Response." Journal of Social Structure 13: 1-36. Butts, Carter T., Ryan M. Acton, John R. Hipp, and Nicholas N. Nagle. 2012b. "Geographical Variability and Network Structure." Social Networks 34 (1): 82100. CNN News US. 2012. “Sandy could bring 'catastrophe,' affect 60 million,” CNN U.S, accessed October 29, 2012. (http://www.cnn.com/2012/10/29/us/tropical-weathersandy/index.html.) Dillman, Don A. 2000. Mail and Internet surveys: The Tailored Design Method. Vol. 2. New York: Wiley.

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