A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN

Yu Qiao, Daniel Wiechmann, Elma Kerz


Abstract
‘Fake news’ – succinctly defined as false or misleading information masquerading as legitimate news – is a ubiquitous phenomenon and its dissemination weakens the fact-based reporting of the established news industry, making it harder for political actors, authorities, media and citizens to obtain a reliable picture. State-of-the art language-based approaches to fake news detection that reach high classification accuracy typically rely on black box models based on word embeddings. At the same time, there are increasing calls for moving away from black-box models towards white-box (explainable) models for critical industries such as healthcare, finances, military and news industry. In this paper we performed a series of experiments where bi-directional recurrent neural network classification models were trained on interpretable features derived from multi-disciplinary integrated approaches to language. We apply our approach to two benchmark datasets. We demonstrate that our approach is promising as it achieves similar results on these two datasets as the best performing black box models reported in the literature. In a second step we report on ablation experiments geared towards assessing the relative importance of the human-interpretable features in distinguishing fake news from real news.
Anthology ID:
2020.rdsm-1.2
Volume:
Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
RDSM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–31
Language:
URL:
https://aclanthology.org/2020.rdsm-1.2
DOI:
Bibkey:
Cite (ACL):
Yu Qiao, Daniel Wiechmann, and Elma Kerz. 2020. A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 14–31, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN (Qiao et al., RDSM 2020)
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PDF:
https://aclanthology.org/2020.rdsm-1.2.pdf
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