Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset (2022.naacl-main)
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| Challenge: | rumor detection models have been designed with oversimplifcation and evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. |
| Approach: | They propose a new Benchmark dataset for EArly Rumor Detection based on claims from fact-checking websites and a novel model based upon neural Hawkes process for EARD. |
| Outcome: | The proposed model can guide a generic rumor detection model to make timely, accurate and stable predictions. |
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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 . |
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| Challenge: | Past research has indicated that content-based rumor detection models perform less effectively on unseen rumors. |
| Approach: | They propose to use data split strategies to minimize the effects of temporal concept drift in static datasets during the training of rumor detection methods. |
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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 . |
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| Challenge: | Existing studies on rumour detection are concerned with timing, but few are interested in how early we can detect them. |
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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. |
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RP-DNN: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media (2020.lrec-1)
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| 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. |
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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 . |
It’s about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits (2023.findings-eacl)
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| Challenge: | Current rumor detection benchmarks use random splits as training, development and test sets which results in topical overlaps. |
| Approach: | They propose to use chronological rather than random splits for rumor classification . they propose to always use chronological splits to minimize topical overlaps . |
| Outcome: | The proposed model overestimates performance on four popular rumor detection benchmarks considering chronological instead of random splits. |
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. |
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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. |
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