# Polarization and Fake News: Early Warning of Potential Misinformation Targets

arXiv: https://arxiv.org/abs/1802.01400 · HTML: https://mlsanity.com/paper/1802.01400/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges](https://mlsanity.com/paper/2411.15396/) (2024)
- [A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches](https://mlsanity.com/paper/2109.09014/) (2021)
- [On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions](https://mlsanity.com/paper/2307.11621/) (2023)
- [Exposing and Explaining Fake News On-the-Fly](https://mlsanity.com/paper/2405.06668/) (2024)

## Abstract (arXiv)

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.