# CSI: A Hybrid Deep Model for Fake News Detection

arXiv: https://arxiv.org/abs/1703.06959 · HTML: https://mlsanity.com/paper/1703.06959/

## Key facts

- Cited by 102 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 30 of 102)

- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Bootstrapping Multi-view Representations for Fake News Detection](https://mlsanity.com/paper/2206.05741/) (2022)
- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [Rumor Detection with Self-supervised Learning on Texts and Social Graph](https://mlsanity.com/paper/2204.08838/) (2022)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network](https://mlsanity.com/paper/2303.03387/) (2023)
- [Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification](https://mlsanity.com/paper/2209.13017/) (2022)
- [Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://mlsanity.com/paper/2309.16424/) (2023)
- [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)
- [Robust Domain Misinformation Detection via Multi-modal Feature Alignment](https://mlsanity.com/paper/2311.14315/) (2023)
- [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)
- [Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI](https://mlsanity.com/paper/2410.02609/) (2024)
- [Cross-lingual COVID-19 Fake News Detection](https://mlsanity.com/paper/2110.06495/) (2021)
- [Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset](https://mlsanity.com/paper/2306.02597/) (2023)
- [Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task](https://mlsanity.com/paper/2101.11425/) (2021)
- [Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks](https://mlsanity.com/paper/2212.12621/) (2022)

## Abstract (arXiv)

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.