Community Evolutional Network for Situation Awareness Using Social Media

Social media is important for situational awareness during a disaster. During a disaster, the situation of emergence often changes over time and hence the topics of social media messages generated by social media users also change accordingly. Few studies quantitatively describe the topic evolution of social media during a disaster and the corresponding relationship between topic evolution and disaster process. We address this problem using co-word network analysis and present a new method based on the community evolution of the co-word network to analyze topic evolution over time in social media. The method uses communities of the co-word network in social media to represent topics. Based on the theory of community evolution, a community evolutional network is proposed to support and quantify the evolution of the topics. We implemented the proposed method in a case study, “July 2012 Beijing flood” using the Sina Weibo dataset. Results show that our method can well quantify the evolution process of topics and validate the effectiveness of our method in real-world applications. The method can facilitate the understanding of public expression dynamics during a disaster and be used to reveal the process and stages of a disaster.