Polarization and Fake News: Early Warning of Potential Misinformation Targets

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.

References in corpus (2)

• Controversy and Sentiment in Online News
• It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features

Cited by in corpus (8)

• Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections
• Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language
• The State of Human-centered NLP Technology for Fact-checking
• DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection
• A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)
• Opinion Diversity and Social Bubbles in Adaptive Sznajd Networks
• Rebalancing Social Feed to Minimize Polarization and Disagreement
• Analyzing Behavioral Changes of Twitter Users After Exposure to Misinformation