# Linked Credibility Reviews for Explainable Misinformation Detection

arXiv: https://arxiv.org/abs/2008.12742 · HTML: https://mlsanity.com/paper/2008.12742/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information](https://mlsanity.com/paper/2311.10784/) (2023)
- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)
- [Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection](https://mlsanity.com/paper/2210.08331/) (2022)
- [Towards Language-driven Scientific AI](https://mlsanity.com/paper/2210.15327/) (2022)
- [Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection](https://mlsanity.com/paper/2401.16285/) (2024)

## Abstract (arXiv)

In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection. The CRs serve as building blocks to compose graphs of (i) web content, (ii) existing credibility signals --fact-checked claims and reputation reviews of websites--, and (iii) automatically computed reviews. We implement this architecture on top of lightweight extensions to Schema.org and services providing generic NLP tasks for semantic similarity and stance detection. Evaluations on existing datasets of social-media posts, fake news and political speeches demonstrates several advantages over existing systems: extensibility, domain-independence, composability, explainability and transparency via provenance. Furthermore, we obtain competitive results without requiring finetuning and establish a new state of the art on the Clef'18 CheckThat! Factuality task.