Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks (2023.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
Copied to clipboard
| 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 . |
Exploring Large Language Models for Effective Rumor Detection on Social Media (2025.naacl-long)
Copied to clipboard
| 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. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
Copied to clipboard
| 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. |
Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media (2023.emnlp-main)
Copied to clipboard
| 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. |
Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models do not distinguish genuine users from social bots, and their failure in identifying rumors timely. |
| Approach: | They propose to account for social bots’ behavior and construct a Social Bot-Aware Graph Neural Network to model early propagation of posts and then use it to detect rumors. |
| Outcome: | The proposed method achieves significant improvements over baselines and identifies rumors within 3 hours while maintaining more than 90% accuracy. |
Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)
Copied to clipboard
| 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. |
A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020.emnlp-main)
Copied to clipboard
| 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. |
Exploiting Microblog Conversation Structures to Detect Rumors (2020.coling-main)
Copied to clipboard
| 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 by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
Copied to clipboard
| 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. |
Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)
Copied to clipboard
| 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. |