# Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection

arXiv: https://arxiv.org/abs/2102.02680 · HTML: https://mlsanity.com/paper/2102.02680/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Combining Fact Extraction and Verification with Neural Semantic Matching Networks](https://mlsanity.com/paper/1811.07039/) (2018)
- [DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning](https://mlsanity.com/paper/1809.06416/) (2018)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [BERT for Evidence Retrieval and Claim Verification](https://mlsanity.com/paper/1910.02655/) (2019)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News](https://mlsanity.com/paper/1806.07516/) (2018)
- [Learning from Fact-checkers: Analysis and Generation of Fact-checking Language](https://mlsanity.com/paper/1910.02202/) (2019)
- [Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation](https://mlsanity.com/paper/2001.02214/) (2020)

## Built on by (most cited first, 17 of 17)

- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors](https://mlsanity.com/paper/2403.09747/) (2024)
- [Why Misinformation is Created? Detecting them by Integrating Intent Features](https://mlsanity.com/paper/2407.19196/) (2024)
- [Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms](https://mlsanity.com/paper/2204.13568/) (2022)
- [Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information](https://mlsanity.com/paper/2109.08022/) (2021)
- [BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking](https://mlsanity.com/paper/2502.16181/) (2025)
- [Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2210.05498/) (2022)
- [Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing](https://mlsanity.com/paper/2304.12888/) (2023)
- [Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidences](https://mlsanity.com/paper/2407.07931/) (2024)
- [Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic](https://mlsanity.com/paper/2109.01850/) (2021)
- [LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification](https://mlsanity.com/paper/2507.11310/) (2025)
- [Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language](https://mlsanity.com/paper/2208.12482/) (2022)
- [Fact-checking based fake news detection: a review](https://mlsanity.com/paper/2401.01717/) (2024)
- [Verify as You Go: An LLM-Powered Browser Extension for Fake News Detection](https://mlsanity.com/paper/2603.05519/) (2026)

## Abstract (arXiv)

The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level. Experiments on two real-word datasets show that our model outperforms seven state-of-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at texttturlhttps://github.com/nguyenvo09/EACL2021.