TY - JOUR
T1 - A Survey on Multimodal Disinformation Detection
AU - Alam, Firoj
AU - Cresci, Stefano
AU - Chakraborty, Tanmoy
AU - Silvestri, Fabrizio
AU - Dimitrov, Dimitar
AU - Da San Martino, Giovanni
AU - Shaar, Shaden
AU - Firooz, Hamed
AU - Nakov, Preslav
N1 - Publisher Copyright:
© 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85140749793&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85140749793
SN - 2951-2093
VL - 29
SP - 6625
EP - 6643
JO - Proceedings - International Conference on Computational Linguistics, COLING
JF - Proceedings - International Conference on Computational Linguistics, COLING
IS - 1
T2 - 29th International Conference on Computational Linguistics, COLING 2022
Y2 - 12 October 2022 through 17 October 2022
ER -