The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

The role of social media for collective behaviour development in response to natural disasters

With the emergence of social media, user-generated content from people affected by disasters has gained significant importance. Thus far, research has focused on identifying categories and taxonomies of the types of information being shared among users during times of disasters. However, there is a lack of theorizing with the dynamics of and relationships between the identified concepts. In our current research, we applied probabilistic topic modelling approach to identify topics from Chennai disaster Twitter data. We manually interpreted and further clustered the topics into generic categories and themes, and traced their development over the days of the disaster. Finally, we build a process model to explore an emerging phenomenon on social media during a disaster. We argue that the conditions/activities such as collective awareness, collective concern, collective empathy and support are necessary conditions for people to feel, respond, and act as forms of collective behaviour

Enhancing Disaster Management Through Social Media Analytics To Develop Situation Awareness: What Can Be Learned From Twitter Messages About Hurricane Sandy?

Twitter became an important channel to contribute and consume all kinds of information, especially in times of disasters, when people feel the need for fast, real-time flows of information. Given the wealth of information Twitter provides, that information can be used by practitioners and researchers alike to study what people affected by a disaster talk about, e.g., to develop a situation awareness and to coordinate disaster management accordingly. In our research, we analyze 11 million tweets that deal with hurricane Sandy, one of the strongest hurricanes that ever hit the US east coast in 2012. First, we extract the tweets by narrowing down the hurricane affected path along the US east coast, based on geo-spatial information. Further, drawing on the situation awareness literature and previous coding schemes, we analyze the nature and characteristics of the tweets. Our research reveals that there are significantly more tweets from original sources than from secondary sources and that individuals tend to share valuable personal experiences and observations at the time of disasters. In analyzing those individual level perceptions, we illustrate how one can generate situation awareness at the collective level. This situation awareness will enhance the decision-making of disaster management agencies at the time of uncertain and volatile situations

Presence of Social Presence during Disasters

During emergencies, affected people use social media platforms for interaction and collaboration. Social media is used to ask for help, provide moral support, and to help each other, without direct face-to-face interactions. From a social presence point of view, we analyzed Twitter messages to understand how people cooperate and collaborate with each other during heavy rains and subsequent floods in Chennai, India. We conducted a manual content analysis to build social presence classifiers comprising intimacy and immediacy concepts which we used to train a machine learning approach to subsequently analyze the whole dataset of 1.65 million tweets. The results showed that the majority of the immediacy tweets are conveying the needs and urgencies of affected people requesting for help. We argue that during disasters, the online social presence creates a sense of responsibility and common identity among the social media users to participate in relief activities