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

Ronald Denaux; Martino Mensio; Jose Manuel Gomez-Perez; Harith Alani

IJCAI 2021poster0 citations

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

Ronald Denaux, Martino Mensio, Jose Manuel Gomez-Perez, Harith Alani

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.

Knowledge Representation and Reasoning: Semantic WebAI Ethics, Trust, Fairness: Societal Impact of AIAI Ethics, Trust, Fairness: ExplainabilityNatural Language Processing: NLP Applications and Tools

BibTeX

@inproceedings{ijcai2021p646,
  title     = {Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)},
  author    = {Denaux, Ronald and Mensio, Martino and Gomez-Perez, Jose Manuel and Alani, Harith},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4760--4764},
  year      = {2021},
  month     = {8},
  note      = {Sister Conferences Best Papers},
  doi       = {10.24963/ijcai.2021/646},
  url       = {https://doi.org/10.24963/ijcai.2021/646},
}