# This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News

arXiv: https://arxiv.org/abs/1703.09398 · HTML: https://mlsanity.com/paper/1703.09398/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 30 of 59)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Fake News Detectors are Biased against Texts Generated by Large Language Models](https://mlsanity.com/paper/2309.08674/) (2023)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims](https://mlsanity.com/paper/2204.12294/) (2022)
- [Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models](https://mlsanity.com/paper/2410.20940/) (2024)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Evaluation of Fake News Detection with Knowledge-Enhanced Language Models](https://mlsanity.com/paper/2204.00458/) (2022)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Verifying the Robustness of Automatic Credibility Assessment](https://mlsanity.com/paper/2303.08032/) (2023)
- [Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective](https://mlsanity.com/paper/2105.09985/) (2021)
- [It's All in the Embedding! Fake News Detection Using Document Embeddings](https://mlsanity.com/paper/2304.07781/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Identifying Misinformation from Website Screenshots](https://mlsanity.com/paper/2102.07849/) (2021)
- [Mitigation of Diachronic Bias in Fake News Detection Dataset](https://mlsanity.com/paper/2108.12601/) (2021)
- [A Multi-Label Dataset of French Fake News: Human and Machine Insights](https://mlsanity.com/paper/2403.16099/) (2024)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification](https://mlsanity.com/paper/2402.03780/) (2024)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection](https://mlsanity.com/paper/2407.08952/) (2024)
- [Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels](https://mlsanity.com/paper/2108.12519/) (2021)
- [3HAN: A Deep Neural Network for Fake News Detection](https://mlsanity.com/paper/2306.12014/) (2023)
- [Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term](https://mlsanity.com/paper/2310.17711/) (2023)
- [HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability](https://mlsanity.com/paper/2407.00668/) (2024)
- [An Experimental Comparison of the Most Popular Approaches to Fake News Detection](https://mlsanity.com/paper/2603.25501/) (2026)

## Abstract (arXiv)

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the arguments in its content. Through a unique study of three data sets and features that capture the style and the language of articles, we show that this assumption is not true. Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims.