People Lie, Actions Don't! Modeling Infodemic Proliferation Predictors among Social Media Users

TL;DR

This paper investigates various factors influencing fake news proliferation on social media, identifying key indicators like sentiment polarity, gender, and Twitter engagement metrics through analysis of COVID-19 datasets.

Contribution

It introduces a statistical testing approach to discover new factors associated with fake news, expanding beyond traditional classification models.

Findings

Sentiment polarity and gender significantly relate to fake news.

Twitter engagement metrics like followers, friends, and retweets differ between fake and real news.

Visual media presence shows inconclusive influence on fake news detection.

Abstract

Social media is interactive, and interaction brings misinformation. With the growing amount of user-generated data, fake news on online platforms has become much frequent since the arrival of social networks. Now and then, an event occurs and becomes the topic of discussion, generating and propagating false information. Existing literature studying fake news primarily elaborates on fake news classification models. Approaches exploring fake news characteristics and ways to distinguish it from real news are minimal. Not many researches have focused on statistical testing and generating new factor discoveries. This study assumes fourteen hypotheses to identify factors exhibiting a relationship with fake news. We perform the experiments on two real-world COVID-19 datasets using qualitative and quantitative testing methods. This study concludes that sentiment polarity and gender can significantly identify fake news. Dependence on the presence of visual media is, however, inconclusive. Additionally, Twitter-specific factors like followers count, friends count, and retweet count significantly differ in fake and real news. Though, the contribution of status count and favorites count is disputed. This study identifies practical factors to be conjunctly utilized in the development of fake news detection algorithms.