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This is a Quantitative Study study.

October 13, 2025Open Access

Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election

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Authors

YLYan LiuFirst Affiliated Hospital of Zhengzhou University YLYunpeng LiuShenyang Institute of Automation LZLiang ZhaoXidian University

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Implication

This study demonstrates improved rumor detection accuracy in social media during elections, suggesting semantic analysis combined with graph neural networks is effective.

Key Points

The method significantly improves rumor detection accuracy during the 2024 elections, highlighting its effectiveness.
A dataset from PolitiFact and Twitter was used, focusing specifically on politically relevant rumors.
The approach incorporates semantic analysis with a BERT model and employs graph neural networks for feature aggregation.
This innovative combination outperforms traditional content analysis methods, suggesting a new pathway for addressing misinformation.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68ece2abd1bb2827d129724f https://doi.org/10.48550/arxiv.2503.01394

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Implication

This study demonstrates improved rumor detection accuracy in social media during elections, suggesting semantic analysis combined with graph neural networks is effective.

Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election

Discussion

Member takes

Implication

Key Points

Cite This Study

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