# Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content

arXiv: https://arxiv.org/abs/2403.08391 · HTML: https://mlsanity.com/paper/2403.08391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media](https://mlsanity.com/paper/1610.07363/) (2016)
- [Evaluation of Fake News Detection with Knowledge-Enhanced Language Models](https://mlsanity.com/paper/2204.00458/) (2022)
- [The Grievance Dictionary: Understanding Threatening Language Use](https://mlsanity.com/paper/2009.04798/) (2020)
- [Style Matters! Investigating Linguistic Style in Online Communities](https://mlsanity.com/paper/2209.13114/) (2022)
- [Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics](https://mlsanity.com/paper/2204.00988/) (2022)

## Built on by (most cited first, 1 of 1)

- [What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes](https://mlsanity.com/paper/2406.03390/) (2024)

## Abstract (arXiv)

What if misinformation is not an information problem at all? To understand the role of news publishers in potentially unintentionally propagating misinformation, we examine how far-right and fringe online groups share and leverage established legacy news media articles to advance their narratives. Our findings suggest that online fringe ideologies spread through the use of content that is consensus-based and "factually correct". We found that Australian news publishers with both moderate and far-right political leanings contain comparable levels of information completeness and quality; and furthermore, that far-right Twitter users often share from moderate sources. However, a stark difference emerges when we consider two additional factors: 1) the narrow topic selection of articles by far-right users, suggesting that they cherry pick only news articles that engage with their preexisting worldviews and specific topics of concern, and 2) the difference between moderate and far-right publishers when we examine the writing style of their articles. Furthermore, we can identify users prone to sharing misinformation based on their communication style. These findings have important implications for countering online misinformation, as they highlight the powerful role that personal biases towards specific topics and publishers' writing styles have in amplifying fringe ideologies online.