MM-Claims

A Dataset for Multimodal Claim Detection in Social Media

authored by

Gullal S. Cheema, Sherzod Hakimov, Abdul Sittar, Eric Muller-Budack, Christian Otto, Ralph Ewerth

Abstract

In recent years, the problem of misinformation on the web has become widespread across languages, countries, and various social media platforms. Although there has been much work on automated fake news detection, the role of images and their variety are not well explored. In this paper, we investigate the roles of image and text at an earlier stage of the fake news detection pipeline, called claim detection. For this purpose, we introduce a novel dataset, MM-Claims, which consists of tweets and corresponding images over three topics: COVID- 19, Climate Change and broadly Technology. The dataset contains roughly 86 000 tweets, out of which 3400 are labeled manually by multiple annotators for the training and evaluation of multimodal models. We describe the dataset in detail, evaluate strong unimodal and multimodal baselines, and analyze the potential and drawbacks of current models.

Organisation(s)

L3S Research Centre

External Organisation(s)

German National Library of Science and Technology (TIB)
Jožef Stefan Institute (JSI)

Type

Conference contribution

Pages

962-979

No. of pages

18

Publication date

07.2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Computer Science Applications, Information Systems

Sustainable Development Goals

SDG 13 - Climate Action

Electronic version(s)

https://doi.org/10.48550/arXiv.2205.01989 (Access: Open)
https://doi.org/10.18653/v1/2022.findings-naacl.72 (Access: Open)