| Challenge: | Existing studies on rumour detection are concerned with timing, but few are interested in how early we can detect them. |
| Approach: | They propose a method that integrates reinforcement learning to learn the minimum number of posts required before classifying an event as a rumour. |
| Outcome: | The proposed model detects rumours earlier than state-of-the-art systems while maintaining comparable accuracy. |
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 . |
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. |
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. |
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. |
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. |
Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in automated rumour verification have limited results in real-world scenarios. |
| Approach: | They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels. |
| Outcome: | The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset. |
RP-DNN: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for early rumor detection on social media platforms are limited, incomplete and noisy. |
| Approach: | They propose a novel hybrid neural network architecture which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets. |
| Outcome: | The proposed model achieves state-of-the-art for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors. |
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)
Copied to clipboard
| 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. |
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. |
Can Rumour Stance Alone Predict Veracity? (C18-1)
Copied to clipboard
| Challenge: | Existing studies of automatic veracity classification of social media rumours have not explored the effectiveness of crowd stance to determine veracity. |
| Approach: | They propose to use stance as an additional feature to those commonly used in earlier studies to model the veracity of a rumour using Hidden Markov Models and collective stance information to model a social media rumor. |
| Outcome: | The proposed models outperform those using crowd stance and tweets’ times as the only features for modelling true and false rumours. |