MAPX: An explainable model-agnostic framework for the detection of false information on social media networks

TL;DR

MAPX is an explainable, model-agnostic framework that adaptively aggregates predictions from various models to improve fake news detection on social media, considering feature quality and temporal dynamics.

Contribution

Introduces MAPX, a novel, adaptive, and explainable aggregation framework that enhances fake news detection by integrating multiple models and accounting for feature quality and temporal changes.

Findings

MAPX outperforms state-of-the-art models on benchmark datasets.

The framework is robust across different data quality scenarios.

Extensive experiments validate the effectiveness of MAPX.

Abstract

The automated detection of false information has become a fundamental task in combating the spread of "fake news" on online social media networks (OSMN) as it reduces the need for manual discernment by individuals. In the literature, leveraging various content or context features of OSMN documents have been found useful. However, most of the existing detection models often utilise these features in isolation without regard to the temporal and dynamic changes oft-seen in reality, thus, limiting the robustness of the models. Furthermore, there has been little to no consideration of the impact of the quality of documents' features on the trustworthiness of the final prediction. In this paper, we introduce a novel model-agnostic framework, called MAPX, which allows evidence based aggregation of predictions from existing models in an explainable manner. Indeed, the developed aggregation method is adaptive, dynamic and considers the quality of OSMN document features. Further, we perform extensive experiments on benchmarked fake news datasets to demonstrate the effectiveness of MAPX using various real-world data quality scenarios. Our empirical results show that the proposed framework consistently outperforms all state-of-the-art models evaluated. For reproducibility, a demo of MAPX is available at \href{https://github.com/SCondran/MAPX_framework}{this link}