---
title: "Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)"
authors:
  - "Ronald Denaux"
  - "Martino Mensio"
  - "Jose Manuel Gomez-Perez"
  - "Harith Alani"
doi: "10.24963/ijcai.2021/646"
url: "https://ijcai.org/proceedings/2021/646"
pdf: "https://ijcai.org/proceedings/2021/0646.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4760-4764"
track: "Sister Conferences Best Papers"
keywords:
  - "Natural Language Processing: NLP Applications and Tools"
  - "AI Ethics, Trust, Fairness: Societal Impact of AI"
  - "AI Ethics, Trust, Fairness: Explainability"
  - "Knowledge Representation and Reasoning: Semantic Web"
---

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

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

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4760-4764. DOI: [10.24963/ijcai.2021/646](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.