Misinformation Detection

Misinformation detection is a field of study to detect documents that include falsified information in the form of text, images, videos, etc. The research explores the ideas to detect misinformed facts from social media posts, news articles, videos and related multimedia data.

Relevant publications:

Cheema, G.S., Hakimov, S., Sittar, A., Müller-Budack, E., Otto C. and Ewerth, R., (2022). MM-Claims: A Dataset for Multimodal Claim Detection in Social Media. Findings of the North American Chapter of the Association for Computational Linguistics (Findings of NAACL 2022) PDF Git repo

Müller-Budack, E., Theiner, J., Diering, S., Idahl, M., Hakimov, S. and Ewerth, R., (2021). Multimodal news analytics using measures of cross-modal entity and context consistency. International Journal of Multimedia Information Retrieval, pp.1-15. PDF Git repo

Cheema, G.S., Hakimov, S., Müller-Budack, E. and Ewerth, R., (2021). On the Role of Images for Analyzing Claims in Social Media. In the Proceedings of the CLEOPATRA workshop co-located with The Web Conference (WWW) PDF Git repo

Cheema, G.S., Hakimov, S. and Ewerth, R., (2020) TIB’s Visual Analytics Group at MediaEval’20: Detecting Fake News on Corona Virus and 5G Conspiracy. MediaEval workshop FakeNews task PDF Git repo

Cheema, G.S., Hakimov, S. and Ewerth, R., (2020). Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features. In Proceedings of the Eleventh International Conference of the CLEF Association PDF Git repo