# The Role of User Profile for Fake News Detection

arXiv: https://arxiv.org/abs/1904.13355 · HTML: https://mlsanity.com/paper/1904.13355/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 30 of 41)

- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification](https://mlsanity.com/paper/2209.13017/) (2022)
- [Unified Dual-view Cognitive Model for Interpretable Claim Verification](https://mlsanity.com/paper/2105.09567/) (2021)
- [FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference](https://mlsanity.com/paper/2110.14532/) (2021)
- [Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information](https://mlsanity.com/paper/2109.08022/) (2021)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom](https://mlsanity.com/paper/2310.16579/) (2023)
- [CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification](https://mlsanity.com/paper/2401.06194/) (2024)
- [VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning](https://mlsanity.com/paper/2406.10289/) (2024)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference](https://mlsanity.com/paper/2512.01728/) (2025)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [TopoBERT: Plug and Play Toponym Recognition Module Harnessing Fine-tuned BERT](https://mlsanity.com/paper/2301.13631/) (2023)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://mlsanity.com/paper/2406.11745/) (2024)
- [Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection](https://mlsanity.com/paper/2509.01660/) (2025)
- [FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features](https://mlsanity.com/paper/2109.04835/) (2021)
- [SOMPS-Net : Attention based social graph framework for early detection of fake health news](https://mlsanity.com/paper/2111.11272/) (2021)
- [Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization](https://mlsanity.com/paper/2112.13910/) (2021)
- [Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook](https://mlsanity.com/paper/2203.11669/) (2022)
- [Entity-Aware Dual Co-Attention Network for Fake News Detection](https://mlsanity.com/paper/2302.03475/) (2023)
- [ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information](https://mlsanity.com/paper/2311.10784/) (2023)

## Abstract (arXiv)

Consuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also enables the widespread of fake news. Because of the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the challenging problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.