| 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 . |
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Exploiting Microblog Conversation Structures to Detect Rumors (2020.coling-main)
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| Challenge: | Existing models for rumor detection ignore the conversation structure of tweets . 68% of american adults occasionally read news on social media platforms . however, the credibility of news propagated through social media is questionable due to the lack of editors who can validate it. |
| Approach: | They propose to model Twitter conversation structure by modeling it as a graph to detect rumors by reading tweets that voice other users’ stances on the tweet. |
| Outcome: | The proposed model outperforms baseline models on two rumor datasets and shows that it outperformed several baseline models. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
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| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)
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| Challenge: | Existing methods for detecting rumors on social media neglect the temporal aspect of rumor propagation. |
| Approach: | They propose a method that incorporates temporal information by building a weighted propagation tree and a coding tree. |
| Outcome: | The proposed approach preserves essential structure of rumor propagation while reducing noise. |
Adversary-Aware Rumor Detection (2021.findings-acl)
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| Challenge: | Existing rumor detection models do not detect malicious attacks, e.g., framing. |
| Approach: | They propose a weighted-edge transformer-graph network and position-aware Adversarial Response Generator to improve the vulnerability of detection models. |
| Outcome: | The proposed framework achieves state-of-the-art on various rumor detection tasks and maintains performance under adversarial learning. |
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (P18-1)
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| Challenge: | Existing methods for detecting rumors are difficult to implement and require a lot of effort. |
| Approach: | They propose two recursive neural models that follow tweets' propagation layouts to learn discriminative features from tweets and generate more powerful representations for rumors detection. |
| Outcome: | The proposed models perform better than state-of-the-art approaches on two public Twitter datasets and show superior performance on detecting rumors at very early stage. |
Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media (2023.emnlp-main)
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| Challenge: | Existing detection models for rumors detection are poor interpretability and lack the textual content to detect rumors. |
| Approach: | They propose a framework that analyzes the textual content and propagation paths of rumors on social media and provides multi-perspective prediction explanations. |
| Outcome: | The proposed framework defends against malicious attacks and provides prediction explanations on three public datasets. |
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)
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| Challenge: | Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies . |
| Approach: | They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV. |
| Outcome: | The proposed model outperforms existing methods on two benchmark datasets. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations (P19-1)
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| Challenge: | Existing approaches to learn from social-media conversations have been proposed to identify and contain fake news shared on social media platforms. |
| Approach: | They propose to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. |
| Outcome: | The proposed models outperform the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively. |
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 . |