# Fake News Early Detection: An Interdisciplinary Study

arXiv: https://arxiv.org/abs/1904.11679 · HTML: https://mlsanity.com/paper/1904.11679/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 29 of 29)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Interpretable Multimodal Misinformation Detection with Logic Reasoning](https://mlsanity.com/paper/2305.05964/) (2023)
- [Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning](https://mlsanity.com/paper/2203.11720/) (2022)
- [Robust Domain Misinformation Detection via Multi-modal Feature Alignment](https://mlsanity.com/paper/2311.14315/) (2023)
- [MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models](https://mlsanity.com/paper/2408.11871/) (2024)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection](https://mlsanity.com/paper/2304.09421/) (2023)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach](https://mlsanity.com/paper/2402.06443/) (2024)
- [Domain-Independent Deception: A New Taxonomy and Linguistic Analysis](https://mlsanity.com/paper/2402.01019/) (2024)
- [Multitask Learning for Class-Imbalanced Discourse Classification](https://mlsanity.com/paper/2101.00389/) (2021)
- [Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2210.15214/) (2022)
- [Linguistic-style-aware Neural Networks for Fake News Detection](https://mlsanity.com/paper/2301.02792/) (2023)
- [FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features](https://mlsanity.com/paper/2109.04835/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [Leveraging Social Interactions to Detect Misinformation on Social Media](https://mlsanity.com/paper/2304.02983/) (2023)
- [Written and spoken corpus of real and fake social media postings about COVID-19](https://mlsanity.com/paper/2310.04237/) (2023)
- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)
- [Disinformation, Stochastic Harm, and Costly Effort: A Principal-Agent Analysis of Regulating Social Media Platforms](https://mlsanity.com/paper/2106.09847/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [Testing the Robustness of a BiLSTM-based Structural Story Classifier](https://mlsanity.com/paper/2201.02733/) (2022)
- [A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model](https://mlsanity.com/paper/2202.01907/) (2022)
- [Fake News Detection Using Majority Voting Technique](https://mlsanity.com/paper/2203.09936/) (2022)
- [Multilingual Disinformation Detection for Digital Advertising](https://mlsanity.com/paper/2207.10649/) (2022)
- [No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation](https://mlsanity.com/paper/2303.18049/) (2023)
- [Exposing and Explaining Fake News On-the-Fly](https://mlsanity.com/paper/2405.06668/) (2024)

## Abstract (arXiv)

Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this paper, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.