A Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters

The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and non-ambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters

PARAMETRIZED EVENT ANALYSIS FROM SOCIAL NETWORKS

The growth of data in social networks facilitate demand for data analysis. The field of event detection is of increasing interest to researchers. Events from real life are actively discussed in the virtual space. Event detection results can be used in a variety of applications, from digital marketing to collecting data about natural disasters. Thereby, researchers face the emergence of new algorithms along with the improvement of existing solutions in the event detection field. This paper proposes improvements to the SEDTWik (Segment-based Event Detection from Tweets using Wikipedia) algorithm. The SEDTWik algorithm is designed to detect events without contextual guidance. The overall SEDTWik detection process excludes the perspective of a topic, or multi-topic, guided (or semi-supervised) event detection approach. As a result, some interesting narrowly focused events are not detected as they are weakly relevant in a broader context (e.g., Wikipedia) although acquiring relevance within a conditioned context. Therefore, there is a need for an adaptive perspective where data is to be analysed against a set of narrower topics of interest. This paper shows that SEDTWik gains expressive power after being extended with multi-topic semi-supervision. The evaluation of the current proposal uses the well-known corpora with labeled events, Events2012. In the Events2012 dataset used notation category for events, meaning that events are combined by a certain topic. SEDTWik with topic dictionaries was checked across all categories. In the main part of the article, it is also explained the process of topic dictionary construction from Events2012 labeled tweets. At this stage of the research, in all tasks unigrams were used. SEDTWik with dictionaries showed improved accuracy, and more events were found within a certain category