Explainable Misinformation Detection from Text: A Critical Look

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With the proliferation of misinformation on the web, automatic methods for detecting misinformation are becoming an increasingly important subject of study. If automatic misinformation detection is applied in a real-world setting, it is necessary to validate the methods being used. Large language models (LLMs) have produced the best results among text-based methods. However, fine-tuning such a model requires a significant amount of training data, which has led to the automatic creation of large-scale misinformation detection datasets. In this paper, we explore the biases present in one such dataset for misinformation detection in English, NELA-GT-2019. We find that models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. Furthermore, we use SHAP to interpret the outputs of a fine-tuned LLM and validate the explanation method using our inherently interpretable baseline. We critically analyze the suitability of SHAP for text applications by comparing the outputs of SHAP to the most important features from our logistic regression models.
Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning
Subtitle of host publication34th Joint Benelux Conference, BNAIC/Benelearn 2022, Mechelen, Belgium, November 7–9, 2022, Revised Selected Papers
EditorsToon Calders, Celine Vens, Jefrey Lijffijt, Bart Goethals
Place of PublicationCham
ISBN (Electronic)9783031391446
ISBN (Print)9783031391439
Publication statusPublished - 2023

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Cham
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


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