Detecting COVID-19-related conspiracy theories in tweets

Peskine, Youri; Alfarano, Giulio; Harrando, Ismail; Papotti, Paolo; Troncy, Raphael
MediaEval 2021, MediaEval Benchmarking Initiative for Multimedia Evaluation Workshop, 13-15 December 2021 (Online Event)

Misinformation in online media has become a major research topic the last few years, especially during the COVID-19 pandemic. Indeed, false or misleading news about coronavirus have been characterized as an infodemic1 by theWorld Health Organization, because of how fast it can spread online. A considerable vector of spreading misinformation is represented by conspiracy theories. During thischallenge, we tackled the problem of detecting COVID-19-related conspiracy theories in tweets. To perform this task, we used different approaches such as a combination of TFIDF and machine learning, transformer-based neural networks or Natural Language Inference. Our best model obtains a MCC score of 0.726 for the main task on the validation set.

Type:
Conference
Date:
2021-12-13
Department:
Data Science
Eurecom Ref:
6753
Copyright:
© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MediaEval 2021, MediaEval Benchmarking Initiative for Multimedia Evaluation Workshop, 13-15 December 2021 (Online Event)

PERMALINK : https://www.eurecom.fr/publication/6753