Discovering the core semantics of event from social media

© 2015 Elsevier B.V. As social media is opening up such as Twitter and Sina Weibo,1 large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis

Understanding Network Dynamics in Flooding Emergencies for Urban Resilience

Many cities around the world are exposed to extreme flooding events. As a result of rapid population growth and urbanization, cities are also likely to become more vulnerable in the future and subsequently, more disruptions would occur in the face of flooding. Resilience, an ability of strong resistance to and quick recovery from emergencies, has been an emerging and important goal of cities. Uncovering mechanisms of flooding emergencies and developing effective tools to sense, communicate, predict and respond to emergencies is critical to enhancing the resilience of cities. To overcome this challenge, existing studies have attempted to conduct post-disaster surveys, adopt remote sensing technologies, and process news articles in the aftermath of disasters. Despite valuable insights obtained in previous literature, technologies for real-time and predictive situational awareness are still missing. This limitation is mainly due to two barriers. First, existing studies only use conventional data sources, which often suppress the temporal resolution of situational information. Second, models and theories that can capture the real-time situation is limited. To bridge these gaps, I employ human digital trace data from multiple data sources such as Twitter, Nextdoor, and INTRIX. My study focuses on developing models and theories to expand the capacity of cities in real-time and predictive situational awareness using digital trace data. In the first study, I developed a graph-based method to create networks of information, extract critical messages, and map the evolution of infrastructure disruptions in flooding events from Twitter. My second study proposed and tested an online network reticulation theory to understand how humans communicate and spread situational information on social media in response to service disruptions. The third study proposed and tested a network percolation-based contagion model to understand how floodwaters spread over urban road networks and the extent to which we can predict the flooding in the next few hours. In the last study, I developed an adaptable reinforcement learning model to leverage human trace data from normal situations and simulate traffic conditions during the flooding. All proposed methods and theories have significant implications and applications in improving the real-time and predictive situational awareness in flooding emergencies