Quantifying correlations between information overload and fake news during COVID-19 pandemic: a Reddit study with BERT model approach

2026-01-01

arXiv.org

AI Summary

This study addresses the lack of scalable methods to automatically quantify information overload (IOL) and its association with misinformation dissemination in large-scale social media data. The authors propose a novel framework that integrates BERTopic for topic modeling with the Gini index—used here for the first time as a proxy measure of IOL—and employs the FakeBERT classifier to detect fake news, enabling a systematic analysis of their interplay within Reddit communities during the COVID-19 pandemic. The findings reveal a significant positive correlation between global-level IOL and the prevalence of fake news, while community-level associations exhibit notable heterogeneity. This approach offers an extensible, quantitative pathway for understanding how the complexity of information environments influences the spread of false information.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Information overload (IOL) is a well-known and devastating phenomenon that alters the performance of carrying out all types of tasks. It has been shown that in the media space, IOL can contribute to news fatigue and news avoidance, which often leads to the proliferation of fake news posts on social networks. However, there is a lack of automatic methods that can be used to track IOL in large datasets. In this study, we investigate whether the Gini index calculated from the distribution of topics obtained via the BERTopic model can be considered a proxy for IOL. We test our assumptions on a set of Reddit communities related to the COVID-19 pandemic and obtain a significant global correlation between the Gini index and the fraction of fake news detected by the FakeBERT classifier. However, at the community level, the correlation analysis results are ambiguous.

Problem

• information overload
• fake news
• social media
• COVID-19 pandemic
• automatic tracking

Innovation

• information overload
• BERTopic
• Gini index
• fake news detection
• social media analysis