Proceedings

Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 4760-4764.

https://doi.org/10.24963/ijcai.2021/646

Abstract

This paper summarises work where we combined semantic web technologies with deep learning systems to obtain state-of-the art explainable misinformation detection. We proposed a conceptual and computational model to describe a wide range of misinformation detection systems based around the concepts of credibility and reviews. We described how Credibility Reviews (CRs) can be used to build networks of distributed bots that collaborate for misinformation detection which we evaluated by building a prototype based on publicly available datasets and deep learning models.

Ronald Denaux, Martino Mensio, Jose Manuel Gomez-Perez, and Harith Alani. 2021. Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract). In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4760–4764. https://doi.org/10.24963/ijcai.2021/646
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards