Detecting COVID-19-related conspiracy theories in tweets

Peskine, Youri; Alfarano, Giulio; Harrando, Ismail; Papotti, Paolo; Troncy, Raphaël
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:
Conférence
Date:
2021-12-13
Department:
Data Science
Eurecom Ref:
6753
Copyright:
CEUR

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