Papers with rumor detection
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor (2020.aacl-main)
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| Challenge: | Existing models to classify rumors have low precision and are time consuming. |
| Approach: | They propose a multiloss hierarchical biLSTM model with an attenuation factor that can extract deep information from limited quantities of text. |
| Outcome: | The proposed model can extract deep information from limited quantities of text. |
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
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| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
| Approach: | This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit . |
| Outcome: | This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection . |
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks (2021.eacl-main)
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| Challenge: | Social networks face a major challenge in the form of rumors and fake news . rumor detection is suboptimal due to its rapidity and spread of information . |
| Approach: | They propose a semantic oppositeness model that captures elements of discord . they show that it is more resistant to variances introduced by randomness . |
| Outcome: | The proposed model achieves state-of-the-art on rumor detection task with extensive experiments on recent data sets. |
Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)
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| Challenge: | Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features. |
| Approach: | They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies. |
| Outcome: | The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets . |
Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection (2021.findings-acl)
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| Challenge: | Existing rumor detection methods provide detection labels while ignoring their explanation. |
| Approach: | a novel model is proposed to automatically classify rumors using Wikipedia documents . the model combines objective facts and subjective views to verify rumours . |
| Outcome: | a new model outperforms existing models on real-world Twitter datasets . the proposed model combines objective facts and subjective views to verify rumor . |
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
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| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
Factuality Assessment as Modal Dependency Parsing (2021.acl-long)
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| Challenge: | a critical step towards factuality assessment is to determine the factuality of events in text. |
| Approach: | They propose a modal dependency parsing task that assesses the factuality of events in text . they crowdsource a large-scale data set annotated with modal dependence structures . |
| Outcome: | The proposed model outperforms the pipeline model in factuality assessment . the proposed model is based on a crowdsourced dataset . |
Exploring Large Language Models for Effective Rumor Detection on Social Media (2025.naacl-long)
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| Challenge: | Large-scale contexts hinder LLMs’ reasoning abilities while moderate contexts perform better for LLM. |
| Approach: | They propose a semantic-propagation collaboration-base framework that integrates small language models with LLMs for effective rumor detection. |
| Outcome: | The proposed framework bridges the gap between LLMs and LLM in facing long, structured data and offers a novel solution for rumor detection on social media. |
Cross-Topic Rumor Detection using Topic-Mixtures (2021.eacl-main)
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| Challenge: | Existing work on rumor detection models has explored network structures, propagation paths, user credibility and fusion of heterogeneous data. |
| Approach: | They propose a method that adapts a rumor detection model trained on source to target topics to make rumour predictions. |
| Outcome: | The proposed method outperforms baseline debiasing methods in a cross-topic setting. |
Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning (2024.emnlp-main)
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| Challenge: | Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection. |
| Approach: | They propose a new multi-task learning framework that mines latent intentions and rumor semantic features . they propose to use event-level and intent-level strategies to establish cognitive anchors . |
| Outcome: | The proposed framework improves the effectiveness of rumor detection and addresses the challenges present in the field. |
Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning (2022.coling-1)
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| Challenge: | Existing rumor detection models assume the same training and testing distributions and can not cope with the continuously changing social network environment. |
| Approach: | They propose a Continual Prompt-Tuning RD framework which avoids catastrophic forgetting of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. |
| Outcome: | The proposed framework avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. |
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (2021.emnlp-main)
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| Challenge: | Existing models for text classification are limited in performance, resulting in poor rumor detection. |
| Approach: | They propose to use Chinese microblogs to detect rumors using pre-trained language models and auxiliary features such as comments to mask co-attention. |
| Outcome: | The proposed model outperforms the state-of-the-art on Weibo20 and three existing social media datasets. |
CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection (2024.lrec-main)
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| Challenge: | Existing multimodal rumor detection models overlook sample difficulty and order when training . Existing models overlook text-level difficulty, image-level and multimodal difficulty when training samples . |
| Approach: | They propose a curriculum learning framework that uses fine-grained fusion to detect rumors . they propose fusion-based methods that combine text and images to enhance semantic cohesion . |
| Outcome: | The proposed framework outperforms state-of-the-art models on English and Chinese benchmark datasets. |
FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)
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| Challenge: | Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics. |
| Approach: | They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection. |
| Outcome: | The proposed model improves on three public real-world datasets. |
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (2021.acl-long)
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| Challenge: | Existing studies on rumor detection focus on text content and propagation structure . however, the uncertainty caused by unreliable relations in propagation structures is common . |
| Approach: | They propose a Bayesian-based model that captures propagation uncertainty for rumor detection. |
| Outcome: | The proposed model achieves better performance than baseline methods on rumor detection and early rumour detection tasks. |
Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks (2023.emnlp-main)
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| Challenge: | Existing research on rumor detection challenges the expressive power of text encoding sequences, and insufficient mining of semantic structural information. |
| Approach: | They propose a Crowd Intelligence-based semantic feature learning module to capture textual content’s sequential and hierarchical features and a knowledge-based structural mining module that leverages ChatGPT for knowledge enhancement. |
| Outcome: | The proposed system achieves performance improvement in rumor detection tasks validating the effectiveness and rationality of using large language models as auxiliary tools. |
Cross-domain Rumor Detection via Test-Time Adaptation and Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches focus on within-domain tasks, resulting in suboptimal performance in cross-domain scenarios due to domain shifts. |
| Approach: | They propose a framework that incorporates both single-domain model and target graph adaptation strategies tailored to the unique requirements of cross-domain rumor detection. |
| Outcome: | The proposed framework surpasses existing methods in rumor detection on social media. |
Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)
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| Challenge: | rumor detection has been reshaped by large language models (LLMs) this paper proposes a Cognition-Interaction-Behavior (CIB) framework for rumour detection based on collective intelligence . |
| Approach: | They propose a Cognition-Interaction-Behavior framework for rumor detection based on collective intelligence and explore synergistic relationship between LLMs and collective intelligence in rumour governance. |
| Outcome: | The proposed framework unifies existing methods and reveals synergistic relationship between LLMs and collective intelligence in rumor governance. |
Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods for rumor detection are limited in labeled data, but social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. |
| Approach: | They propose a framework for rumor detection with Graph Supervised Contrastive Learning that heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumors detection. |
| Outcome: | The proposed framework heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection. |
Debunking Rumors on Twitter with Tree Transformer (2020.coling-main)
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| Challenge: | Existing methods for rumor detection follow tree edges or treat all posts fully-connected during feature learning. |
| Approach: | They propose a new rumor detection model based on tree transformer to better utilize user interactions in the dialogue . they propose to use post-level self-attention to aggregate the intra-/inter-subtree stances . |
| Outcome: | The proposed model improves rumor detection performance on social media conversations . it is based on a conversation tree that encodes important information indicative of credibility . |
Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model (2025.coling-main)
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| Challenge: | Pretrained Language Models excel in various Natural Language Processing tasks, but performance on social media applications like rumor detection remains suboptimal. |
| Approach: | They propose a pretraining strategy to infuse information from propagation structures into pretrained language models to capture interactions of stance and sentiment crucial for rumor detection. |
| Outcome: | The proposed model outperforms existing methods on social media applications and significantly improves rumor detection performance. |
Semantic Reshuffling with LLM and Heterogeneous Graph Auto-Encoder for Enhanced Rumor Detection (2025.coling-main)
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| Challenge: | Current methods struggle against complex propagation influenced by bots, coordinated accounts, and echo chambers, which fragment information and increase risks of misjudgments. |
| Approach: | They propose a framework that integrates metapath-based rumor reconstruction and narrative reordering to detect rumors. |
| Outcome: | The proposed model outperforms existing methods and is highly accurate and robust. |
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)
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| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
| Approach: | They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations . |
| Outcome: | The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples . |
A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020.emnlp-main)
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| Challenge: | Existing methods to rumor detection ignored dynamical evolution of an event and failed to capture its unique features in different states. |
| Approach: | They propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation. |
| Outcome: | The proposed framework can significantly improve the rumor detection accuracy in comparison with some strong baseline systems. |
SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing methods for rumor detection on social media focus on static graphs, ignoring dynamic and incremental propagation . rumour detection on the social media platform is crucial to mitigating harmful effects of rumors. |
| Approach: | They propose a sliding window and memory-augmented attention model for rumor detection . they use a dynamic propagation graph and memory to capture the long-term dependency . |
| Outcome: | The proposed model is compared with the state-of-the-art models on two public datasets. |
Interpretable Short Video Rumor Detection Based on Modality Tampering (2024.lrec-main)
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| Challenge: | Existing methods to detect rumors from the perspective of modality tampering are labor-intensive and time-consuming. |
| Approach: | They propose a short video rumor detection framework that integrates modality tampering detection and inter-modal matching into a model to detect modality-tampers and interpretability mechanisms to make the results more reasonable. |
| Outcome: | The proposed model improves on the short video rumor dataset by 4.6%-12% compared with other models and can explain whether the short clip is a rumour or not through the perspective of modality tampering. |
Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection (2023.findings-emnlp)
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| Challenge: | Existing rumor detection models neglect the semantic coherence between text and image components in multimodal posts . Existing models neglect incomplete modalities in single modal posts, such as missing text or images . |
| Approach: | They propose a framework for incomplete modality rumor detection that captures semantic consistency between text and image pairs while enhancing model generalization to incomplete modalities within individual posts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media. |
LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)
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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li
| Challenge: | Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora. |
| Approach: | They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues. |
| Outcome: | The proposed model outperforms existing SOTA on three datasets. |