Publikationen (FIS)


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

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.

L3S Research Centre
External Organisation(s)
German National Library of Science and Technology (TIB)
Jožef Stefan Institute (JSI)
Conference contribution
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Computational Theory and Mathematics, Computer Science Applications, Information Systems
Sustainable Development Goals
SDG 13 - Climate Action
Electronic version(s) (Access: Open) (Access: Open)