A Survey on Multimodal Disinformation Detection

Firoj Alam, Stefano Cresci, Tanmoy Chakraborty*, Fabrizio Silvestri, Dimitar Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

48 Citations (Scopus)

Abstract

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation – (i) factuality, and (ii) harmfulness –, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.

Original languageEnglish
Pages (from-to)6625-6643
Number of pages19
JournalProceedings - International Conference on Computational Linguistics, COLING
Volume29
Issue number1
Publication statusPublished - 2022
Event29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of
Duration: 12 Oct 202217 Oct 2022

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