Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI

TL;DR

This paper presents a comprehensive fake news detection method for under-resourced languages by integrating social context and content features, utilizing various machine learning techniques, and applying explainable AI for model analysis.

Contribution

It introduces a novel approach combining social context and content features for fake news detection in under-resourced languages, with extensive experiments and explainability analysis.

Findings

Ensemble learning achieved 0.99 F1 score.

Fine-tuned models outperformed monolingual models with 0.94 F1.

Explainable AI identified key features influencing detection accuracy.

Abstract

The proliferation of fake news has emerged as a significant threat to the integrity of information dissemination, particularly on social media platforms. Misinformation can spread quickly due to the ease of creating and disseminating content, affecting public opinion and sociopolitical events. Identifying false information is therefore essential to reducing its negative consequences and maintaining the reliability of online news sources. Traditional approaches to fake news detection often rely solely on content-based features, overlooking the crucial role of social context in shaping the perception and propagation of news articles. In this paper, we propose a comprehensive approach that integrates social context-based features with news content features to enhance the accuracy of fake news detection in under-resourced languages. We perform several experiments utilizing a variety of methodologies, including traditional machine learning, neural networks, ensemble learning, and transfer learning. Assessment of the outcomes of the experiments shows that the ensemble learning approach has the highest accuracy, achieving a 0.99 F1 score. Additionally, when compared with monolingual models, the fine-tuned model with the target language outperformed others, achieving a 0.94 F1 score. We analyze the functioning of the models, considering the important features that contribute to model performance, using explainable AI techniques.