Enhancing Disinformation Detection with Explainable AI and Named Entity Replacement

TL;DR

This paper improves disinformation detection by using explainable AI to identify and remove non-informative features and replace named entities, leading to better generalization and higher accuracy on external datasets.

Contribution

It introduces a novel preprocessing approach involving named entity replacement guided by explainability methods to enhance disinformation classification models.

Findings

Performance on external data increased by 65.78% after preprocessing.

Removing URLs and emoticons improves model focus on relevant features.

Named entity replacement reduces model bias and improves generalization.

Abstract

The automatic detection of disinformation presents a significant challenge in the field of natural language processing. This task addresses a multifaceted societal and communication issue, which needs approaches that extend beyond the identification of general linguistic patterns through data-driven algorithms. In this research work, we hypothesise that text classification methods are not able to capture the nuances of disinformation and they often ground their decision in superfluous features. Hence, we apply a post-hoc explainability method (SHAP, SHapley Additive exPlanations) to identify spurious elements with high impact on the classification models. Our findings show that non-informative elements (e.g., URLs and emoticons) should be removed and named entities (e.g., Rwanda) should be pseudo-anonymized before training to avoid models' bias and increase their generalization capabilities. We evaluate this methodology with internal dataset and external dataset before and after applying extended data preprocessing and named entity replacement. The results show that our proposal enhances on average the performance of a disinformation classification method with external test data in 65.78% without a significant decrease of the internal test performance.