Polarization and Fake News: Early Warning of Potential Misinformation Targets

TL;DR

This paper presents a framework to identify polarizing social media content to predict potential fake news topics, achieving high accuracy and aiding misinformation mitigation.

Contribution

It introduces a novel method that uses user behavior characteristics to predict and detect fake news topics before they spread widely.

Findings

77% accuracy in predicting misinformation-prone topics

91% accuracy in fake news classification when using the new feature

Effective early warning system for misinformation on social media

Abstract

Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, "predicting" future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.