A Modality-level Explainable Framework for Misinformation Checking in Social Networks

TL;DR

This paper proposes a multimodal, explainable framework for automatic misinformation detection on social networks, aiming to improve interpretability and effectiveness over traditional content-only methods.

Contribution

It introduces a modality-level explainable misinformation classifier that leverages social features and provides interpretable inferences, addressing limitations of existing approaches.

Findings

Multimodal information encoding improves classifier performance.

Explainable mechanisms enhance interpretability and completeness.

Framework supports more trustworthy misinformation assessment.

Abstract

The widespread of false information is a rising concern worldwide with critical social impact, inspiring the emergence of fact-checking organizations to mitigate misinformation dissemination. However, human-driven verification leads to a time-consuming task and a bottleneck to have checked trustworthy information at the same pace they emerge. Since misinformation relates not only to the content itself but also to other social features, this paper addresses automatic misinformation checking in social networks from a multimodal perspective. Moreover, as simply naming a piece of news as incorrect may not convince the citizen and, even worse, strengthen confirmation bias, the proposal is a modality-level explainable-prone misinformation classifier framework. Our framework comprises a misinformation classifier assisted by explainable methods to generate modality-oriented explainable inferences. Preliminary findings show that the misinformation classifier does benefit from multimodal information encoding and the modality-oriented explainable mechanism increases both inferences' interpretability and completeness.