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TWITTER AND DISASTERSDhiraj Murthy a & Scott A. Longwell ba Department of Sociology and Anthropology , BowdoinCollege , 7000 College Station, Brunswick , ME ,04011 , USAb Bowdoin College , Brunswick , ME , 04011 , USAPublished online: 25 Jun 2012.

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Dhiraj Murthy & Scott A. Longwell

TWITTER AND DISASTERS

The uses of Twitter during the 2010

Pakistan floods

This research explores the specific use of the prominent social media website Twitterduring the 2010 Pakistan floods to examine whether users tend to tweet/retweetlinks from traditional versus social media, what countries these users are tweetingfrom, and whether there is a correlation between location and the linking of tra-ditional versus social media. The study finds that Western users have an overwhelm-ing preference for linking to traditional media and Pakistani users have a slightpreference for linking to social media. The study also concludes that authoritiesand hubs in our sample have a significant preference for linking to socialmedia rather than traditional media sites. The findings of this study suggest thatthere is a perceived legitimacy of social media during disasters by users inPakistan. Additionally, it provides insights into how social media may be –albeit minimally – challenging the dominant position of traditional media indisaster reporting in developing countries.

Keywords Twitter; social network analysis; authority; network ties;social factors; new media

(Received 7 October 2011; final version received 3 May 2012)

Introduction

Social media has become an important source of information for some individualsduring and in the aftermath of disasters. In particular, Twitter, the most promi-nent social media service, has become more commonly used by Internet-usingindividuals to keep abreast of breaking news regarding disasters and updateswhich may be coming in frequently throughout the day. Twitter and similarmicroblog social media sites are inherently built for individual users to subscribeto flows of information. In the case of Twitter, users who are interested inbreaking news ‘follow’ the Twitter feeds of traditional news media. What is

Information, Communication & Society Vol. 16, No. 6, August 2013, pp. 837–855

# 2013 Taylor & Francis

http://dx.doi.org/10.1080/1369118X.2012.696123

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also particularly unique to social media is that users can then elect to follow theupdates of users who they feel are close to a disaster. In the case of Twitter, a userwho is interested in a particular disaster searches for it. They then can choose tofollow the tweets of ordinary users ‘reporting’ on the disaster (i.e. citizen jour-nalists) or they can simply read the feeds of these users.

This research uses the 2010 floods in Pakistan as a case study. The floods inPakistan caused enormous loss of life, significant environmental destruction, anda large-scale humanitarian crisis. The United Nations quickly labeled the situ-ation in Pakistan as a ‘catastrophe’; 1,802 were reported dead and 2,994injured (Associated Press of Pakistan 2010). The United Nations World FoodProgramme estimated that 1.8 million people ‘were in dire need of water,food and shelter’(The Irish Times 2010). In the case of the floods, the vast majorityof the affected population were digital have-nots prior to the flooding. Nationalbroadband penetration was a mere 0.31% (International TelecommunicationUnion 2010). Additionally, the areas affected are predominantly home toUrdu language speakers, rather than English language speakers. By earlyAugust 2010, the floods became a worldwide trending topic on Twitter,placing it in a list of the top 10 trending topics on Twitter’s homepage. Thefloods were also the third most popular trending topic in 2010 in Twitter’sNews Events category (Twitter.com 2010). Trending topics appear as linkswithin the profile pages of all Twitter users. Additionally, given this placementwithin Twitter as well as search engine results, users are known to be guided totrending topics (Abrol & Khan 2010).

Though other studies have offered analyses of disaster situations usingTwitter (Hughes et al. 2008; Hughes & Palen 2009; Kireyev et al. 2009; Palenet al. 2010; Vieweg et al. 2010; Doan et al. 2011; Tucker 2011), this study ismore unique in its focus on a disaster in a developing country. The searchterm ‘Pakistan’ was chosen for data collection as it was the exact text stringof the Twitter trending topic. There are other tags relating to the Pakistanfloods including ‘PKFloodRelief’. However, due to its status as trending topic,the most substantial traffic regarding the floods was associated with this officialtrending topic. In this study, we collected 42,814 tweets spanning a 50-hourperiod containing the search term ‘Pakistan’; 27,193 tweets linked to an externalsite. These data are analyzed to explore: (1) how Twitter is being used in disastersituations; (2) whether users tend to tweet/retweet links from traditional orsocial media; and (3) what countries had the highest frequency of tweetsduring the initial aftermath of the 2010 Pakistan floods.

This article will first briefly introduce the disasters literature regardingTwitter. It will then explain the methods of data collection including whatsearch term was used and how user location of tweets was discerned. Lastly,results from the study will be presented which reveal that tweets more fre-quently linked to traditional media rather than social media sources. However,for Pakistan-based users, there is a slight preference for linking to social

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media. Influential users in the network also had a marked preference for linkingto social media. The implications of this study are significant for two key reasons.First, they provide evidence that there may be a perceived legitimacy of socialmedia during disasters by users in developing countries. This potentially presentsa challenge to the ‘elite hold’ of traditional media (Meraz 2009). The secondimplication is that despite tweets from users in Western countries far outnum-bering those by Pakistani users, the latter remain important users to thenetwork. They are not completely overshadowed by Western users. This poten-tially signals a challenge to the dominant position Western media have in theirtraditional role of disseminating information regarding disasters in developingcountries (Gaddy & Tanjong 1986).

Twitter and disasters

Before examining the role of Twitter in the Pakistan floods in specific, it is usefulto present an overview of the emergent literature on Twitter and disasters. AsPalen et al. (2010) note, ‘it is meaningful to begin to think of Twitter andother social media as serving different functions among different user groupsduring different events’. As Kiriyev et al. (2009) observe, research on theusage of social media and disaster events has been growing, covering a rangeof sites including social networking sites, photo repositories, and microbloggingsites. Twitter has also been used for disaster relief efforts by major non-govern-mental organizations (NGOs) including the Red Cross (Tucker 2011). The dis-cussion was of natural disasters on Twitter or a critical turning point of bringingthe microblogging site to mainstream attention (with the US presidential electionas a very important other; Tumasjan et al. 2010). As Murthy (2011) argues, thedowning of US Airways flight 1549 in the Hudson River in New York in 2009and Twitter’s use in covering the story legitimized the site as a journalisticspace. Specifically, Janis Krums, a passenger on a passing Midtown ferry tooka picture of the downed aircraft on his iPhone and circulated it on Twitter viathe site’s photo sharing portal, TwitPic. This happened well before any newscrews arrived in many major news media used his iPhone picture in printmedia with the Associated Press eventually purchasing distribution rights.MSNBC had him on the phone within 30 minutes of him posting on Twitter.

The October 2007 wildfires in Southern California were perhaps the firstnatural disaster that put Twitter on the map. As Hughes and Palen (2009,p. 1) note, Twitter was used ‘to inform citizens of the time-critical informationabout road closures, community evacuations, shifts in fire lines, and shelter infor-mation’. Another man-made disaster in which Twitter became important and,indeed, controversial were the Mumbai bomb blasts of 2008 where everydayIndians were tweeting about which hotels have been taken over by armedgunmen, where fires were still burning, and where shots had been fired

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(Murthy 2010). Because tweets are so short, researchers found them to be amedium especially well suited to communicating real-time information duringdisasters (Hughes & Palen 2009). As Palen et al. (2010) note, ‘the implicationsof social media are significant for mass emergency events’. In their case study ofthe 2009 Red River Valley flood, they found significant cases of individuals tweet-ing about their disaster experience as well as about aid organizations involved(Palen et al. 2010). Though this was also the case with Pakistan, this was notthe case with the Urdu language tweets analyzed. Indeed, the volume of tweetsin Urdu about the flood was so low that we did not obtain enough data to runsignificant statistical analysis (as will be discussed in the subsequent section).

Method

Identifying a sample

Because Twitter is a global medium which includes tweets in many languages, weran a beta test to see what impact Urdu language tweets were having during thePakistan floods. Based on Pakistan’s low broadband penetration and base ofUrdu-language speakers, it was expected that tweets regarding the floodswould be from Western countries (and to some extent India), rather thancitizen journalists on the ground in Pakistan. However, we tested for the possi-bility of a significant cohort of Urdu language Twitter users in Pakistan. Data wascollected from 7–29–8/3/2010 for the search term ‘flood’ in Urdu. A searchwas run for ‘ ’, the Urdu word for ‘flood’. In determining the searchterms, ‘ ’ (‘heavy rains’) and (‘Pakistan’) were also run. Thesearch for heavy rains yielded only eight tweets and the search term for Pakistanyielded more tweets regarding negative perceptions of British Prime MinisterDavid Cameron by Pakistanis rather than referring to the flood. For the Urdusearch of ‘flood’, a total of 146 tweets were collected. One hundred and seven-teen of these contained external URLs (see Table 1). Of note, only one of thesetweets was a retweet. The Urdu data set consisted of a total of 44 unique Twitterusers. Of the top 20 users tweeting, 11 were organizations and 9 were individ-uals. The most tweets (18) were sent by an individual, who happened to be aspammer, and the second most sent by Daily Pakistan (17), a news site.

Though the vast majority of links were to news sites (excepting for spam),one user, a woman in Islamabad, Pakistan, was acting as a citizen journalist. Sheposted three pictures from the Swat valley area and posted them to her Facebookalbum and sent tweets linking to it. Each of the pictures had a description of aneyewitness account of what was happening in the area. Because of the limited sizeof this data set (N ¼ 44), we determined that the investigation of Urdu languagetweets was beyond the remit of this research and was best suited for a futurequalitative ethnographic study.

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Sample

For our study, tweets were collected by accessing the Twitter search API duringthe periods when the text string ‘Pakistan’ was most active. The data set is com-posed of 50 hours worth of tweets broken into five 10-hour slices gatheredduring August 2010 (08/4, 08/11, 08/13–14, 08/15, and 08/16). A cross-section of time and day segments was selected to capture tweets from aroundthe world and not just from North American time zones. A total of 42,814tweets were collected. In addition to this data set, we created a ‘relationship’subset which included only tweets with an at-sign mention (e.g. @user). Thisincluded retweets. In this subset, our data consisted of 13,259 unique userswho were directing tweets to each other or retweeting.

In the relationship data set, each tweet was coded as an arc with the tweetoriginator (ego) as the sender and the Twitter user being mentioned (alter) as therecipient. Different nominal values were assigned to arcs depending on the typeof at-sign mention (1: recipient was the first user mentioned at the start of thetweet, 2: recipient was mentioned at the start of the tweet but not first, 3: reci-pient was mentioned somewhere else in the body of the tweet, 4: recipient wasmentioned first in a retweet, 5: recipient was mentioned in a retweet but notfirst). We used boyd et al.’s (2010, p. 3) rubric for determining retweets(‘RT: @’, ‘retweeting @’, ‘retweet @’, ‘(via @)’, ‘RT (via @)’, ‘thx @’,‘HT @’, and ‘r @’).

One of our modalities by which to better understand the large-scale networkwhich our data set represented was through social network analysis (SNA) (Scott2000; Carrington et al. 2005), a social scientific method to visually map net-works. As the data set was stored to reflect relationships between Twitter

TABLE 1 Urdu language tweet frequency by external links.

Website Number of tweets with linking URLs

Various spam websites 22

Daily Pakistan 12

Jang Daily Newspaper, Pakistan 8

British Broadcasting Corporation Urdu website 6

One Pakistan News 5

News Urdu 5

Voice of America Dari (Persian) 4

GEOnews Urdu 4

Personal user’s Facebook pictures 3

Islam Times Newspaper photo gallery 3

Facebook album of Channel 5 TV Pakistan 2

Other sites 43

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users following the nominal values mentioned in the previous section, we wereable to create a visual map with country and city as variables. A total of 33,141arcs involving 13,259 unique vertices (i.e. unique users) were analyzed (seeFigure 2). The SNA software Pajek was used to help visualize network ties ofTwitter users tweeting about Pakistan as a whole (especially to gauge thedensity of the network). Pajek was also used to discern which Twitter userswere considered to be ‘hubs’ and ‘authorities’, concepts used in SNA todenote vertices which are considered to be an authority (i.e. a source of infor-mation to be trusted) and a hub (i.e. an important clearinghouse to find infor-mation from authorities). In the case of the Internet, as Kleinberg (1999)notes, hubs have the tendency to link out to authorities, but the inverse is notnecessarily true. Additionally, he notes that authorities may not link heavily toother authorities. Network partitions of the 100 most ‘important’ hubs and auth-orities (as calculated by Pajek) were created. These data were used to rendervisualizations which show not only intra-hub and intra-authority connections,but also show the density of links from them to the network as a whole. Thepurpose of this was to gain macro representations of our sample. We alsoused these network partitions to test for correlation between a Twitter user’scountry of origin and the status of hub or authority.

The top 100 domains to which Twitter users in our data set linked to werehand coded. Outbound links were classified into four categories of media (Tra-ditional, Social, Aggregator, and governmental organization/NGO). ‘Traditional’media is defined as the websites of print and broadcast journalism. ‘Social’ media is

FIGURE 1 Tweet-embedded links corresponding to one of the top 100 domains.

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defined as user-generated media (blogs, videos, geo-location micro-blogs, ques-tion/answer sites, and other user-generated news). Some non-user generatednews sites such as the Huffington Post are coded as social as these sites rely onblog-based contributors and are not print or broadcast-based. ‘Aggregator’ sitesare defined as portals bringing together news and information from both traditionaland social media (aggregator sites generally prioritized traditional media sourcesover social ones). ‘Governmental organizations/NGOs’ were coded based oneither governmental affiliation or NGO status. For transparency, categorizationof the top 50 domains by frequency is listed in Table 2.

URL and domain extraction

Many tweets contain hyperlinks (URLs) referencing content on other sites. Inorder to conserve characters, Twitter usually shortens full URLs automatically

FIGURE 2 The 10 most important authorities and their connections to the whole network.

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TABLE 2 Top 50 most frequently linked to domains by category.

Domain Frequency Type

1 www.bbc.co.uk 1,080 Traditional

2 news.yahoo.com 737 Aggregator

3 www.guardian.co.uk 501 Traditional

4 www.cnn.com 465 Traditional

5 news.google.com 461 Aggregator

6 www.facebook.com 456 Social

7 www.youtube.com 454 Social

8 www.nytimes.com 443 Traditional

9 twitpic.com 386 Social

10 www.pheedcontent.com 381 Aggregator

11 www.boston.com 358 Traditional

12 tribune.com.pk 306 Traditional

13 twitter.com 297 Social

14 www.dawn.com 266 Traditional

15 www.reuters.com 230 Traditional

16 edition.cnn.com 219 Traditional

17 www.twitionger.com 216 Social

18 engiish.aijazeera.net 213 Traditional

19 www.huffingtonpost.com 210 Social

20 www.state.gov 202 Govt./NGO

21 www.googie.com 195 Traditional

22 www.spiegei.de 162 Traditional

23 www.unicef.org.uk 162 Govt./NGO

24 www.npr.org 150 Traditional

25 www.tagesschau.de 143 Traditional

26 www.telegraph.co.uk 140 Traditional

27 www.unicef.org 132 Govt./NGO

28 www.trampmagazine.com 130 Traditional

29 www.onepakistan.com 129 Social

30 humanitariannews.org 124 Social

31 biogs.bettor.com 114 Social

32 earthobservatory. nasa.gov 113 Govt./NGO

33 www.newzfor.me 109 Aggregator

34 uk.reuters.com 106 Traditional

35 timesofindia.indiatimes.com 99 Traditional

36 www.aiertnet.org 96 Social

Continued

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with a URL shortening service such as ‘bit.ly’. For example, a 59-character URLlinking to YouTube from our sample was shortened to a 20-character bit.ly URL.We determined the URL’s final destination in the case that the URL was of theshortened type (e.g. bit.ly). Since it is possible for a shortened URL to refer toanother shortened URL (and so on), we allowed for up to three redirects ifnecessary. Each URL was truncated at the first occurrence of a forward slash,question mark, or colon and the resulting URL list and domain list were pro-cessed in order to obtain frequencies for distinct URLs and domains, fromwhich frequency-sorted tables were generated (see Table 2).

User location of Tweets

The location of Twitter users is an important variable for this study. However, asthe location field is freeform in structure, a user may enter anything from a well-formed location (e.g. ‘Rome, Lazio, IT’) to less location-specific data (e.g. ‘Thejungle baby!’). Occasionally, user locations also contain coordinate data derivedfrom a GPS-enabled mobile device or IP address approximation. To accommo-date this variance, a raw location was first checked to see if it matched theform of coordinate data (e.g. 26.252527,106.867631). Coordinate data wereassumed to contain a comma separating latitude from longitude and this waspassed to the Yahoo! PlaceFinder API. Assuming a match was found, thereturned location data was stored in a standardized ‘city, state code, countrycode’ format (e.g. New York, NY, US). Raw location data not fitting the

TABLE 2 Continued

Domain Frequency Type

37 www.cbc.ca 93 Traditional

38 www.businessweek.com 90 Traditional

39 www.abc.net.au 86 Traditional

40 www.msnbc.msn.com 85 Traditional

41 search.yahoo.com 84 Other

42 search2know.com 81 Aggregator

43 freedomist.com 80 Social

44 www.dec.org.uk 78 Govt./NGO

45 www.voanews.com 78 Traditional

46 www.unicef.org 77 Govt./NGO

47 www.thesundaytimes.co.uk 76 Traditional

48 www.csmonitor.com 74 Traditional

49 www.unhcr.org 73 Govt./NGO

50 www3.treasurecoastnews.org 70 Social

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above procedure for coordinates were then passed in their entirety to the Yahoo!PlaceMaker API as it recognizes zip/postal codes, country/state codes, and somecolloquial names, such as ‘The Big Apple’ and ‘New England’. When the APIfinds a match, it returns information including the central coordinates of thematched region (i.e. the center of a town, state, or country). These data werethen used to clean up our data set.

Results

The data represented in Table 3 demonstrate that traditional media was the mostfrequently linked media form during the 2010 Pakistan floods. Though 12 web-sites out of the top 50 and 24 out of the top 100 are social, the representation oftraditional media websites in the hundred most frequently linked to domains wasalmost double of social media (with 44/100 domains representing traditionalmedia). Additionally, the most frequently linked to aggregator sites in the top100 (i.e. Google News and Pheedcontent) overweighed traditional mediasources over social media sources. Therefore, we found that the representationof traditional media in the external links is even higher in comparison of socialmedia when aggregator sites are taken into account.

In Table 4, tweets are broken down by country and link type (based ondomain type). The ‘other’ category aggregates tweets from 97 unique countries.Figure 1 suggests geographic difference emerging with a preference to socialmedia sites in Pakistan. Links pointing to traditional media were posted farmore often across all top link-posting countries, with frequencies usually dou-bling that of the next closest category (Figure 1). Of note, Pakistan was theone exception to this trend, where links to social domains were more prevalentthan links to traditional ones. Links to news aggregators were also fairly commonwith the exception of the UK, which had a proportionally small frequency ofaggregator links (see Table 4).

Pajek was used to isolate the 100 most important hubs and authorities. AsFigure 2 reveals, we find the authorities within this network have a high frequencyof interconnections with the network as a whole. Each line represents an arc (i.e.an interaction between users based on an @ mention). Directionality has been

TABLE 3 Top 100 domain frequencies broken down by category.

Category Number Median frequency Mean frequency SD

Traditional 44 81.5 154.27 186.11

Social 24 69 135.21 132.10

Govt./NGO 19 55 74.68 45.72

Aggregator 11 64 189.18 232.89

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removed to make the frequency of links clearer. However, there are both inwardand outward arcs. In concurrence with Kleinberg (1999), our study also found alow level of intra-hub links. However, the visualization of hubs is similar to that ofauthorities (though not included) in that each hub represents a dense fabric of con-nections back to the network as a whole. The network of Pakistan-related tweets iscohesive and has clear hubs and authorities. Hubs and authorities were also used toexamine user frequency by country (see Tables 5 and 6)

Table 4 reveals that the United States has the greatest frequency of tweets(4,473), Pakistan is second (2,440) and the UK marginally behind (2,261). Inter-estingly, as Table 5 shows, the frequency of tweets from Pakistan itself was thehighest in terms of tweets by authorities despite the United States being the

TABLE 4 Top tweeting countries and breakdown by media category (for the top 100

domains listed by ISO country codes).

User country

Media type

Aggregator Govt./NGO Other Social Traditional Total

– 885 448 21 969 2,150 4,473

US 362 311 7 486 1,274 2,440

PK 348 106 60 895 852 2,261

GB 77 197 29 252 822 1,377

IN 94 22 1 37 228 382

CA 29 35 2 65 250 381

ID 102 7 1 81 105 296

DE 23 22 0 27 205 277

NL 28 58 0 16 146 248

AU 6 8 0 24 74 112

FR 9 8 0 24 64 105

IT 3 8 1 76 15 103

AE 1 7 1 45 33 87

SA 1 16 0 39 26 82

IR 1 21 0 14 40 76

BR 6 12 0 1 54 73

JP 0 13 0 6 31 50

CN 7 7 0 14 20 48

MY 7 4 0 7 27 45

KR 5 13 0 12 13 43

KW 0 1 0 35 3 39

TH 6 2 0 7 21 36

ES 4 6 0 12 12 34

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highest in terms of sheer number of tweets. Five of the top 10 hubs as well asauthorities are Pakistan-based Twitter users, making Pakistan the most dominantcountry in terms of top-level representation. This is an interesting finding in thatPakistani Twitter users have a higher chance of hub/authority status.

The top 100 authorities listed one of seven countries for user-definedlocation. The tweet frequency by authority versus non-authority from thesecountries is shown in Figure 3. A chi-square reveals that the relative portion

TABLE 5 Frequency of tweets by authority versus non-authority by user country.

User country

User category

Authority Non-authority Total

PK 677 3,205 3,882

GB 145 2,216 2,361

US 70 4,589 4,659

CA 48 839 887

IR 34 96 130

CN 20 68 88

SA 16 115 131

Total 1,010 11,128

TABLE 6 Frequency of tweets by hub versus non-hub by user country.

User Country

User category

Hub Non-hub Total

PK 937 2,945 3,882

GB 167 2,194 2,361

CA 52 835 887

CN 52 36 88

AE 45 152 197

IR 42 88 130

US 18 4,641 4,659

SA 11 120 131

IT 5 171 176

ID 3 480 483

MY 3 89 92

AU 2 202 204

IN 1 815 816

Total 1,338 12,768

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of tweets sent from authorities in each group varied significantly (x2¼ 794.6, df¼ 4, p , 0.0001). The ‘other’ category includes all tweets from Ireland, China,and South Africa. Although users from the United States posted the highest fre-quency of aggregate tweets, Pakistan had the most tweets sent from 1 of the top100 authorities and the second-most total aggregate tweets (Figure 3). Though asignificant percentage of tweets from UK users (country code GB) were fromauthorities, a chi-square test shows that Pakistan’s larger percentage of auth-ority-posted tweets is significant relative to the UK (x2 ¼ 163.9, df ¼1, p, 0.0001). These are important findings in that though Western Twitterusers were much greater in total volume (especially when the UK, UnitedStates, and Canada are viewed as a bloc), users from those countries were notgenerally viewed as authorities.

We see Pakistan also dominating hubs (see Table 6) with 937 tweets associ-ated with Pakistani hubs. The UK’s 167 tweets are significant, but the standarddeviation between the third-highest, Canada, and the next three countries ismarginal. Additionally, the United States has a mere 18 tweets associated witha hub. The top 100 hubs consisted of 13 user-defined country locations. Aswith the authorities–country relationship, a chi-square reveals that there is noconsistent percentage of tweets posted by hubs across countries (x2 ¼ 1465,df ¼ 4, p , 0.0001). The ‘other’ category aggregates all tweets from IN, ID,AU, AE, IT, SA, IR, MY, and CN. Like the analysis of authorities, Pakistanhad the greatest frequency of tweets posted by hubs (Figure 4). Pakistan’s relativeportion of hub-posted tweets was also significantly greater than those of UKusers (x2 ¼ 293.7, df ¼ 1, p , 0.0001). However, the United States had amarginal level of hub-posted tweets, numbering less than the aggregated

FIGURE 3 Tweets (authority/non-authority) by user country.

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countries in the ‘other’ category. The data shows that the United States andCanada are weak both in terms of authority and hub status.

As Tables 7 and 8 demonstrate, another finding of our study is that thoughlinks to traditional media are clearly dominant, the authorities and hubs of thenetwork show a preference to social media links over traditional media linkswith 319 versus 245 for authorities and 473 versus 346 for hubs. We foundthis to be of significant interest as just examining the aggregate number oftweets only has a limited value in that it is the authority of the tweet thatincreases its likelihood of impact on the tweet recipient, thereby raising the ques-tion of the impact of social media. Another conclusion, and it is one that isbeyond the scope of this paper, is that the position of Pakistan as leader bothin terms of authority in hubs of flood information potentially reverses historicaltrends which position Western media sources as more ‘elite’ in the role ofdisseminating information regarding disasters in underdeveloped countries(Gaddy & Tanjong 1986).

A chi-square reveals that the percentage of links posted by authorities isgreater within the social category than in the traditional one (x2¼ 160.2,df ¼ 1, p , 0.0001). A chi-square also reveals that the percentage of linksposted by hubs is indeed greater within the social category than in the traditionalone (x2 ¼ 263.1, df ¼ 1, p , 0.0001). Authorities and hubs displayed verysimilar linking patterns (Figures 5 and 6). Overall, traditional media linkswere clearly most frequent, followed distantly by social media links and then

FIGURE 4 Tweets (hub/non-hub) by user country.

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TABLE 8 Breakdown of media type by hub versus non-hub.

Media type

User category

Hub Non-hub Total

Social 473 2,772 3,245

Traditional 346 6,442 6,788

Govt./NGO 73 1,346 1,419

Aggregator 33 2,048 2,081

Other 0 126 126

Total 925 12,734

TABLE 7 Breakdown of media type by authority versus non-authority.

Media type

User category

Authority Non-authority Total

Social 319 2,926 3,245

Traditional 245 6,543 6,788

Govt./NGO 74 1,345 1,419

Aggregator 15 2,066 2,081

Other 0 126 126

Total 653 13,006

FIGURE 5 The frequency of tweet-embedded links, grouped by link type and partitioned

by authority status.

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by aggregator links. Despite this disparity, both authorities and hubs posted morelinks to social media domains than any other type, including traditional media.Authorities and hubs also appeared to link more often to government organiz-ations/NGOs than to aggregator sites even though the total frequency ofaggregator links was greater.

Conclusion

In the case of the 2010 Pakistan floods, tweets more frequently linked totraditional versus social media sources. However, in the case of tweets fromPakistani users, the margin between social and traditional is small, but signalsa potential challenge to the ‘elite hold’ of traditional media (Meraz 2009). Amajor finding of this study is that the top 100 hubs and authorities of thenetwork represented in our sample had a significant preference for choosingto link to social versus traditional media in their tweets. Though the study’sresults showed a continuing preference of total users in the sample to link to tra-ditional media, these findings regarding hubs and authorities were unexpected.They provide insights into information source preference on Twitter duringdisasters.

FIGURE 6 The frequency of tweet-embedded links, grouped by link type and partitioned

by hub status.

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Additionally, this potentially signals not only an increased legitimization ofsocial media but also of the legitimization of social media in developing countrieswhich experience disasters. Another key finding is that though Twitter usersfrom Western countries (especially the UK, United States, and Canada) consti-tute a large percentage of total aggregate tweets in our data set, tweets fromPakistani Twitter users conferred more authority status. In other words, infor-mation and links contained in tweets by Pakistani users have a higher likelihood tobe considered more authoritative during the 2010 Pakistan floods. We feel thatthese are important findings and ones which warrant further investigation as wellgiven the lack of substantial literature on authority in disasters on Twitter.Though ‘[n]one of the celebrated advances in science and technology seem tohave done much to arrest the force of natural disasters’ (Erikson 1979,p. 200), social media is one means not only to keep information flow high,but to also potentially give authority to users sending tweets from disaster-affected underdeveloped countries. Additionally, the preferences in terms ofmedia sources linked in tweets during the 2010 Pakistan floods highlight a poten-tial trend in the importance of social media-based information during disasters indeveloping countries.

Acknowledgements

The authors wish to thank Andrew Currier of Bowdoin College’s InformationTechnology Department for programming assistance with the Twitter-baseddata collector. Dhiraj Murthy also wishes to acknowledge participants of the2011 International Communications Association conference (where an earlierversion of this paper was presented) for their useful feedback.

References

Abrol, S. & Khan, L. (2010) ‘TWinner: understanding news queries with geo-content using Twitter’, in Proceedings of the 6th Workshop on Geographic Infor-mation Retrieval, 28–29 January 2010, eds R. Purves, P. Clough, andC. Jones, ACM, Zurich, Switzerland, Article Number 10.

Associated Press of Pakistan (2010) 1,802 Confirmed Dead in Floods: Kaira, AssociatedPress of Pakistan, Islamabad.

boyd, d. m., Golder, S. & Lotan, G. (2010) ‘Tweet, tweet, retweet: conversationalaspects of retweeting on Twitter’, 43rd Hawaii International Conference onSystem Sciences, Koloa, Kauai, Hawaii.

Carrington, P. J., Scott, J. & Wasserman, S. (2005) Models and Methods in SocialNetwork Analysis, Cambridge University Press, Cambridge.

T W I T T E R A N D D I S A S T E R S 8 5 3

Dow

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ded

by [

Fac

Lat

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mer

ican

a de

Cie

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cial

es]

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9:16

17

Aug

ust 2

015

Doan, S., Ho Vo, B.-K. & Collier, N. (2011) ‘An analysis of Twitter messages in the2011 Tohoku earthquake’, Lecture Notes of the Institute for Computer Sciences,Social Informatics and Telecommunications Engineering, Vol. 91, pp. 58–66,[Online] Available at: http://www.springerlink.com/content/p52u7w1t68640064/?MUD=M (21 January 2012).

Erikson, K. T. (1979) In the Wake of the Flood, George Allen & Unwin, London.Gaddy, G. D. & Tanjong, E. (1986) ‘Earthquake coverage by the Western Press’,

Journal of Communication, vol. 36, no. 2, pp. 105–112.Hughes, A. L. & Palen, L. (2009) ‘Twitter adoption and use in mass convergence

and emergency events’, in Proceeding of the 6th International ISCRAMConference, Gothenburg, Sweden, May 2009, eds J. Landgren and S. Jul, [Online]Available at: http://www.iscram.org/ISCRAM2009/papers/ (16 February2011).

Hughes, A. L., Palen, L., Sutton, J., Liu, S. B. & Vieweg, S. (2008) ‘“Site-Seeing” indisaster: an examination of on-line social convergence’, 5th InternationalISCRAM Conference, Washington, DC.

International Telecommunication Union (2010) ‘Fixed broadband subscriptions’,[Online] Available at: http://www.itu.int/ITU-D/ict/statistics/ (12 August2011).

Kireyev, K., Palen, L. & Anderson, K. (2009) ‘Applications of Topics Models toAnalysis of Disaster-Related Twitter Data’, NIPS workshop on applicationsfor topic models: text and beyond, Whistler, Canada, 12 November 2009.

Kleinberg, J. (1999) ‘Hubs, authorities, and communities’, ACM Computing Surveys,vol. 31, no. 4es, Article number 5.

Meraz, S. (2009) ‘Is there an elite hold? Traditional media to social media agendasetting influence in blog networks’, Journal of Computer-Mediated Communi-cation, vol. 14, no. 3, pp. 682–707.

Murthy, D. (2011) ‘Twitter: microphone for the masses’, Media Culture Society,vol. 33, no. 5, pp. 779–789.

Palen, L., Starbird, K., Vieweg, S. & Hughes, A. (2010) ‘Twitter-basedinformation distribution during the 2009 Red River Valley flood threat’,Bulletin of the American Society for Information Science and Technology, vol. 36,no. 5, pp. 13–17.

Scott, J. (2000) Social Network Analysis: A Handbook, Sage, London.The Irish Times (2010) ‘Anger builds as disaster affects three million’, The Irish

Times, Wednesday, 8 August.Tucker, C. (2011) ‘Social media, texting play new role in response to disasters:

preparedness, communication targeted’, The Nation’s Health, vol. 41, no. 4,pp. 1–18.

Tumasjan, A., Sprenger, T. O., Sandner, P. G. & Welpe, I. M. (2010) ‘Predictingelections with Twitter: what 140 characters reveal about political sentiment’,4th International AAAI Conference on Weblogs and Social Media (ICWSM),Washington, DC.

Twitter.com (2010) 2010 Trends on Twitter, Twitter Inc., San Francisco.

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Vieweg, S., Hughes, A. L., Starbird, K. & Palen, L. (2010) ‘Microblogging duringtwo natural hazards events: what twitter may contribute to situational aware-ness’, in Proceedings of the 28th International Conference on Human Factors inComputing Systems, Atlanta, Georgia, 10–15 April 2010, ACM, Atlanta, GA,pp. 1079–1088.

Dhiraj Murthy is Assistant Professor of Sociology at Bowdoin College, USA. His

research interests include social media, virtual organizations, online commu-

nities, and digital ethnography. He has recently published his work in Sociology,

Media Culture and Society, the European Journal of Cultural Studies, and Ethnic

and Racial Studies. He has a forthcoming book with Polity Press titled Twitter:

Social Communication in the Twitter Age. Address: Department of Sociology and

Anthropology, Bowdoin College, 7000 College Station, Brunswick, ME 04011,

USA. [email: dmurthy@bowdoin.edu]

Scott A. Longwell recently graduated from Bowdoin College with an under-

graduate degree in Biochemistry and a minor in Computer Science. He was a

research fellow supervised by Dhiraj Murthy from 2010–2011. Address: Bowdoin

College, Brunswick, ME 04011, USA. [email: slongwel@bowdoin.edu]

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