Abstract
In times of crisis involving disasters or other extreme events, victims of these events use social media to share information about their situation. The user-generated content contains vast amounts of valuable information, albeit mostly hidden, regarding the victims’ needs, the urgency of supplies, and their situation following the disaster. Especially when drawn from adversely affected areas, these insights are useful for coordinating relief and rescue activities among communities and organizations devoted to improving conditions and saving lives. The insights are quite valuable for humanitarian operations to develop a real time understanding of the situation even before they arrive at the ground. Hence, it is imperative that we develop innovative methods for harnessing the potential of the user-generated content, in order to make disaster relief efforts more effective. Time is an important dimension in humanitarian relief activities, since providing assistance and supplies to victims in a timely manner helps save lives and mitigate the effects of the disaster. However, while extracting time-related information from user-generated social media content is highly
important, the relevant scholarly literature has not yet explored this problem. With this in mind, in addition to exploring methodological aspects related to identifying and extracting real-time information and analyzing disaster-related social media data through a theoretical lens, this dissertation contributes to the understanding of the importance of extracting real-time information in the context of a disaster.
Our literature review revealed that there has been very little attention devoted to the use of social media in disasters in information systems (IS) research. For the empirical part of this dissertation, we focused on two different natural catastrophes: For Study 1, we applied a manual content analysis method to analyze the social media data related to Hurricane Sandy through the theoretical lens of situation awareness. For Study 2, we applied a supervised machine learning approach to analyze the media data related to the Chennai floods through the theoretical lens of social presence. For Study 3, we applied the unsupervised topic modeling method to analyze social media data related to the Chennai floods for the purpose of understanding the emerging phenomena during disasters. Furthermore, inductively derived reflections and findings
based on the empirical studies (Study 1 and 2) further helped clarify the challenges and opportunities associated the methods point of view and social media data point of view.
The research we conducted offered support for the idea of a method of automatically extracting information from social media content in real time and served as a basis for the exploration of this idea. We developed a time-indicating dictionary—a time wordlist (T-wordlist) to automatically process and extract time-relevant information of needs and urgencies during disasters from social media content. This research extends a social media analytics method—the dictionary-based approach—by developing the T-wordlist to extract the time-relevant information from the social media content.
Overall this dissertation contributes to current research in two ways. First, research findings confirm the theoretical explanations of situation awareness and social presence and shed light on emerging phenomena. Most importantly, this dissertation extends the methodological aspects by analyzing the theoretical concept of social presence, since thus far this concept has only been analyzed through survey strategy or by conducting interviews. The research at hand extends this concept further by analyzing Twitter messages and examining why people choose to help one another in times of crisis despite the lack of a personal relationship. Second, the contributions of this dissertation have important implications for practitioners. Since disaster management agencies must handle massive volumes of data during disasters, the Twordlist could be embedded into their systems, resulting in an automatic extraction of
time-relevant information concerning needs and urgencies.
important, the relevant scholarly literature has not yet explored this problem. With this in mind, in addition to exploring methodological aspects related to identifying and extracting real-time information and analyzing disaster-related social media data through a theoretical lens, this dissertation contributes to the understanding of the importance of extracting real-time information in the context of a disaster.
Our literature review revealed that there has been very little attention devoted to the use of social media in disasters in information systems (IS) research. For the empirical part of this dissertation, we focused on two different natural catastrophes: For Study 1, we applied a manual content analysis method to analyze the social media data related to Hurricane Sandy through the theoretical lens of situation awareness. For Study 2, we applied a supervised machine learning approach to analyze the media data related to the Chennai floods through the theoretical lens of social presence. For Study 3, we applied the unsupervised topic modeling method to analyze social media data related to the Chennai floods for the purpose of understanding the emerging phenomena during disasters. Furthermore, inductively derived reflections and findings
based on the empirical studies (Study 1 and 2) further helped clarify the challenges and opportunities associated the methods point of view and social media data point of view.
The research we conducted offered support for the idea of a method of automatically extracting information from social media content in real time and served as a basis for the exploration of this idea. We developed a time-indicating dictionary—a time wordlist (T-wordlist) to automatically process and extract time-relevant information of needs and urgencies during disasters from social media content. This research extends a social media analytics method—the dictionary-based approach—by developing the T-wordlist to extract the time-relevant information from the social media content.
Overall this dissertation contributes to current research in two ways. First, research findings confirm the theoretical explanations of situation awareness and social presence and shed light on emerging phenomena. Most importantly, this dissertation extends the methodological aspects by analyzing the theoretical concept of social presence, since thus far this concept has only been analyzed through survey strategy or by conducting interviews. The research at hand extends this concept further by analyzing Twitter messages and examining why people choose to help one another in times of crisis despite the lack of a personal relationship. Second, the contributions of this dissertation have important implications for practitioners. Since disaster management agencies must handle massive volumes of data during disasters, the Twordlist could be embedded into their systems, resulting in an automatic extraction of
time-relevant information concerning needs and urgencies.
| Original language | English |
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| Qualification | Doctor of Philosophy (PhD) |
| Supervisor(s) |
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| Award date | 1 May 2018 |
| Publisher | |
| Print ISBNs | 978-87-7949-013-0 |
| Publication status | Published - 2018 |