MM-Claims

A Dataset for Multimodal Claim Detection in Social Media

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Institut "Jožef Stefan" (IJS)

Typ

Aufsatz in Konferenzband

Seiten

962-979

Anzahl der Seiten

18

Publikationsdatum

07.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Angewandte Informatik, Information systems

Ziele für nachhaltige Entwicklung

SDG 13 – Klimaschutzmaßnahmen

Elektronische Version(en)

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