Quantifying correlations between information overload and fake news during COVID-19 pandemic: a Reddit study with BERT model approach
This work addresses the need for automatic IOL tracking in social media, but the ambiguous community-level results limit its practical impact.
The study proposes using the Gini index from BERTopic topic distributions as a proxy for information overload (IOL) and finds a significant global correlation with fake news prevalence on COVID-19 Reddit communities, but community-level results are ambiguous.
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.