Detection of dangerous events on social media: a critical review

• Published in: Social network analysis and mining Vol. 12; no. 1; p. 154
• Main Authors: Jamil, M. Luqman, Pais, Sebastião, Cordeiro, João
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 2022; Springer Nature B.V
• Subjects: Applications of Graph Theory and Complex Networks; Bullying; Computer Science; Data Mining and Knowledge Discovery; Digital media; Economics; Game Theory; Hate speech; Humanities; Knowledge bases (artificial intelligence); Law; Methodology of the Social Sciences; Polls & surveys; Review Paper; Sentiment analysis; Social and Behav. Sciences; Social media; Social networks; Statistics for Social Sciences; User behavior; Violence; Xenophobia; Sentiment analysis; Social computing; Event detection; Social network services; Terrorism; Dangerous events; Social media; Extremism
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-022-00980-y

The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentiment, scenario, and action-based dangerous events grouped on their similarities. The existing research and methods related to event detection are surveyed, including some available events datasets and knowledge-base to address the problem. Finally, the survey is concluded with suggestions for future work and possible related challenges.