# Beyond News Contents: The Role of Social Context for Fake News Detection

arXiv: https://arxiv.org/abs/1712.07709 · HTML: https://mlsanity.com/paper/1712.07709/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2017.

## Built on by (most cited first, 30 of 91)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Evaluating Deep Learning Approaches for Covid19 Fake News Detection](https://mlsanity.com/paper/2101.04012/) (2021)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19](https://mlsanity.com/paper/2208.09578/) (2022)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Blessing or curse? A survey on the Impact of Generative AI on Fake News](https://mlsanity.com/paper/2404.03021/) (2024)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification](https://mlsanity.com/paper/2209.13017/) (2022)
- [Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting](https://mlsanity.com/paper/2310.10429/) (2023)
- [A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03545/) (2021)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information](https://mlsanity.com/paper/2109.08022/) (2021)
- [Transforming Fake News: Robust Generalisable News Classification Using Transformers](https://mlsanity.com/paper/2109.09796/) (2021)
- [Verifying the Robustness of Automatic Credibility Assessment](https://mlsanity.com/paper/2303.08032/) (2023)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [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)
- [Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification](https://mlsanity.com/paper/2201.11664/) (2022)
- [Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks](https://mlsanity.com/paper/2212.12621/) (2022)
- [VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning](https://mlsanity.com/paper/2406.10289/) (2024)

## Abstract (arXiv)

Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users to receive authentic information but also help maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.