# Linguistic-style-aware Neural Networks for Fake News Detection

arXiv: https://arxiv.org/abs/2301.02792 · HTML: https://mlsanity.com/paper/2301.02792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 7 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [Causal Understanding of Fake News Dissemination on Social Media](https://mlsanity.com/paper/2010.10580/) (2020)
- [Neural Discourse Structure for Text Categorization](https://mlsanity.com/paper/1702.01829/) (2017)
- [What's Going On in Neural Constituency Parsers? An Analysis](https://mlsanity.com/paper/1804.07853/) (2018)
- [Detecting False Rumors from Retweet Dynamics on Social Media](https://mlsanity.com/paper/2201.13103/) (2022)
- ["This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders](https://mlsanity.com/paper/2202.04752/) (2022)

## Built on by (most cited first, 2 of 2)

- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [Unpacking Hateful Memes: Presupposed Context and False Claims](https://mlsanity.com/paper/2510.09935/) (2025)

## Abstract (arXiv)

We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. The code and data have been publicly available.