MaTED : metadata-assisted Twitter event detection system

Pandya, Abhinay; Oussalah, Mourad; Kostakos, Panos; Fatima, Ummul (2020-06-05)
 
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https://doi.org/10.1007/978-3-030-50146-4_30

Pandya, Abhinay
Oussalah, Mourad
Kostakos, Panos
Fatima, Ummul
Springer Nature
05.06.2020

Pandya A., Oussalah M., Kostakos P., Fatima U. (2020) MaTED: Metadata-Assisted Twitter Event Detection System. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_30

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© Springer Nature Switzerland AG 2020. “This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-50146-4_30.
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doi:https://doi.org/10.1007/978-3-030-50146-4_30
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Abstract

Due to its asynchronous message-sharing and real-time capabilities, Twitter offers a valuable opportunity to detect events in a timely manner. Existing approaches for event detection have mainly focused on building a temporal profile of named entities and detecting unusually large bursts in their usage to signify an event. We extend this line of research by incorporating external knowledge bases such as DBPedia, WordNet; and exploiting specific features of Twitter for efficient event detection. We show that our system utilizing temporal, social, and Twitter-specific features yields improvement in the precision, recall, and DERate on the benchmarked Events2012 corpus compared to the state-of-the-art approaches.

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