| 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 . |
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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. |
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| Challenge: | Existing detection models for rumors detection are poor interpretability and lack the textual content to detect rumors. |
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| Challenge: | Large-scale contexts hinder LLMs’ reasoning abilities while moderate contexts perform better for LLM. |
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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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| 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. |
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
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| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
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| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
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