A Survey on Multimodal Disinformation Detection

• URL: http://arxiv.org/abs/2103.12541v1
• Date: Sat, 13 Mar 2021 18:04:17 GMT
• Title: A Survey on Multimodal Disinformation Detection
• Authors: Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
• Abstract summary: We explore the state-of-the-art on multimodal disinformation detection covering various combinations of modalities. We argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness.
• Score: 33.89798158570927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed the proliferation of fake news, propaganda, misinformation, and disinformation online. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract much more attention, and spread further than simple text. As a result, researchers started targeting different modalities and combinations thereof. As different modalities are studied in different research communities, with insufficient interaction, here we offer a survey that explores the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, audio, video, 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.

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