Detection of dangerous events on social media: a critical review

Author:

Jamil M. LuqmanORCID,Pais SebastiãoORCID,Cordeiro JoãoORCID

Abstract

AbstractThe 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.

Funder

Fundação para a Ciência e a Tecnologia

Portugal 2020 Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Approach for Detecting Dangerous Events from Online Text Using Transformers;2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT);2023-10-26

2. Bibliometric analysis of international publication trends on social media and terrorism by using the Scopus database;Frontiers in Communication;2023-06-13

3. Relevant Tweets Identification from Disaster-related Tweets;2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3);2023-06-08

4. Machine Learning to Classify Religious Communities and Detect Extremism on Social Networks;International Journal of Organizational and Collective Intelligence;2022-10-14

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