ML Anthology

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

Ronald Denaux, Martino Mensio, José Manuél Gómez-Pérez, Harith Alani
IJCAI 2021 pp. 4760-4764
doi:10.24963/IJCAI.2021/646 /ijcai/2021/denaux2021ijcai-weaving/

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.

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Cite

Text

Denaux et al. "Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/646

Markdown

[Denaux et al. "Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/denaux2021ijcai-weaving/) doi:10.24963/IJCAI.2021/646

BibTeX

@inproceedings{denaux2021ijcai-weaving,
  title     = {{Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)}},
  author    = {Denaux, Ronald and Mensio, Martino and Gómez-Pérez, José Manuél and Alani, Harith},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {4760-4764},
  doi       = {10.24963/IJCAI.2021/646},
  url       = {https://mlanthology.org/ijcai/2021/denaux2021ijcai-weaving/}
}