Multiverse: Multilingual Evidence for Fake News Detection

TL;DR

This paper introduces Multiverse, a multilingual evidence feature for fake news detection, demonstrating that cross-lingual information improves classification accuracy across multiple datasets.

Contribution

It proposes a novel multilingual evidence feature for fake news detection and validates its effectiveness through experiments on diverse datasets.

Findings

Multilingual evidence improves fake news detection accuracy.

The proposed feature outperforms baseline models.

Combining linguistic and multilingual features yields significant improvements.

Abstract

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. It is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose Multiverse -- a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. After that, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset showing that in additional combination with linguistic features it yields significant improvements.