# Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

arXiv: https://arxiv.org/abs/1903.09196 · HTML: https://mlsanity.com/paper/1903.09196/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 30 of 32)

- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (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)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection](https://mlsanity.com/paper/2102.02680/) (2021)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection](https://mlsanity.com/paper/2503.21127/) (2025)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals](https://mlsanity.com/paper/2305.11349/) (2023)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)
- [A Weibo Dataset for the 2022 Russo-Ukrainian Crisis](https://mlsanity.com/paper/2203.05967/) (2022)
- [Misinformation Detection using Large Language Models with Explainability](https://mlsanity.com/paper/2510.18918/) (2025)
- [RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2605.10357/) (2026)
- [Leveraging Social Interactions to Detect Misinformation on Social Media](https://mlsanity.com/paper/2304.02983/) (2023)
- [REMOD: Relation Extraction for Modeling Online Discourse](https://mlsanity.com/paper/2102.11105/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks](https://mlsanity.com/paper/2112.00803/) (2021)
- [A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection](https://mlsanity.com/paper/2203.14132/) (2022)
- [Hope Speech Detection on Social Media Platforms](https://mlsanity.com/paper/2212.07424/) (2022)
- [MAPX: An explainable model-agnostic framework for the detection of false information on social media networks](https://mlsanity.com/paper/2409.08522/) (2024)
- [Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social Media](https://mlsanity.com/paper/2410.19193/) (2024)
- [Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning](https://mlsanity.com/paper/2508.10040/) (2025)
- [Explainable Fraud Detection with GNNExplainer and Shapley Values](https://mlsanity.com/paper/2509.12262/) (2025)
- [RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2512.22933/) (2025)

## Abstract (arXiv)

Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.