Flood Twittering: A Marketing and Public Policy Perspective Through the Lens of Actor-Network Theory
Twitter could be claimed as part of the critical social network media, which can respond promptly to any event in real time. Recently, many scholars investigated various aspects of Twitter in disaster management such as the behavior of Twitter users durin
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Currently, it has 107,680 followers (December 6, 2012). Thus, this study focused on the Tweeting network of #Thaiflood through the lens of ANT. The rationale of using ANT in this phenomenon is the need to illustrate disaster management by its nature as a Twitter network. However, previous studies have focused mainly on one aspect of an event such as tweet analysis (Qu et al., 2011), user analysis (Sakakiet al., 2010) or provider analysis (Caragea et al., 2011). Therefore, this paper intends to investigate the Twitter actor-network in a flood situation by using the vocabulary of ANT, including the actor, actor-network, translation, intermediaries and power. A new set of findings that explain the Twitter network could help in conducting efficient disaster management. Furthermore, the ultimate aim of this work responds to the need of Baker (2009, p.114-115), in that “Disaster research begs involvement from marketing scholars. As a function, marketing is fundamentally concerned with needs assessment and fulfillment and with the efficient distribution of resources….If marketing is to make a contribution to disaster preparedness and response, it needs to understand how disaster science views the world, and disaster science needs to understand marketing’s contributions to its worldview.” This study also responds to the intention and request from Finch and Acha (2008), and Kjellberga and Helgesson (2007) in applying and extending the use of ANT in the area of marketing. METHODOLOGY The core data of this study were the tweets from the hash tag #Thaiflood, which were collected in the period of flooding: September 5, 2012 to November 15, 2012 (35 days). There were 1,533 tweets for analysis. This study employed the content analysis method in the data analysis. Four assistant researchers were trained for coding the data from each tweet. In some cases the data were ambiguous, and this was solved by discussion with the authors. A content coding form was constructed for analyzing the data by following the framework of ANT. Actors in the Twitter actor-network were identified by their behavior and actions. Therefore, the actors in this study were #Thaiflood (a focal actor), Twitter users, followers, mobile devices and computers. To depict the actor-network in detail, each tweet was classified. The date of twittering, type of tweets, re-tweet numbers, and location mentioned in tweets would help to explain the process of translation and intermediaries; and the number of re-tweet users and their followers could help in exploring the notion of power in the actor-network. RESULTS AND DISCUSSION According to actor-network theory (ANT), #Thaiflood can be seen as the focal actor in an actor-network that combines human actors, such as the multilayers of #Thaiflood followers, with non-human actors, like smart phones, mobile devices and computers. In general, this actor-network operates actively in communicating flood information, starting from the focal actor’s tweeted messages. Then, the followers re-tweet continually with the help of a no
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