# Interpretable Rumor Detection in Microblogs by Attending to User Interactions

arXiv: https://arxiv.org/abs/2001.10667 · HTML: https://mlsanity.com/paper/2001.10667/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 30 of 44)

- [Transformer for Graphs: An Overview from Architecture Perspective](https://mlsanity.com/paper/2202.08455/) (2022)
- [Attending to Graph Transformers](https://mlsanity.com/paper/2302.04181/) (2023)
- [Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning](https://mlsanity.com/paper/2204.08143/) (2022)
- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning](https://mlsanity.com/paper/2212.01117/) (2022)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks](https://mlsanity.com/paper/2305.10329/) (2023)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [Joint-Relation Transformer for Multi-Person Motion Prediction](https://mlsanity.com/paper/2308.04808/) (2023)
- [A Survey of Graph Transformers: Architectures, Theories and Applications](https://mlsanity.com/paper/2502.16533/) (2025)
- [Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention](https://mlsanity.com/paper/2303.11945/) (2023)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection](https://mlsanity.com/paper/2508.07201/) (2025)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom](https://mlsanity.com/paper/2310.16579/) (2023)
- [LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information](https://mlsanity.com/paper/2502.08888/) (2025)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [TorchGT: A Holistic System for Large-scale Graph Transformer Training](https://mlsanity.com/paper/2407.14106/) (2024)
- [A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection](https://mlsanity.com/paper/2502.14403/) (2025)
- [Sequential Attention Source Identification Based on Feature Representation](https://mlsanity.com/paper/2306.15886/) (2023)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [VGA: Vision and Graph Fused Attention Network for Rumor Detection](https://mlsanity.com/paper/2401.01759/) (2024)
- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
- [Graph Representation Learning with Massive Unlabeled Data for Rumor Detection](https://mlsanity.com/paper/2508.04252/) (2025)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)
- [Unseen Fake News Detection Through Casual Debiasing](https://mlsanity.com/paper/2503.04160/) (2025)

## Abstract (arXiv)

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.