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A multi-modal approach for recognizing fake news and influential nodes in spreading them using deep learning and
Weiliang Zhang1,2, Meina Qian3, Qingfeng Zhang4
1Department of Global Convergence, Kangwon National University, Chuncheon-si, Gangwon-do, 24341, Korea.
Scientific Reports
|February 18, 2026
Summary
This study introduces a novel method combining deep learning and graph clustering to detect social media rumor sources and predict their spread paths. The technique effectively identifies influential spreaders and rumor trajectories with high accuracy.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Artificial Intelligence
Background:
- Rumors spread rapidly on social media, causing significant harm.
- Identifying rumor sources and spread patterns is crucial for mitigation.
- Existing methods may lack the precision to pinpoint influential nodes effectively.
Purpose of the Study:
- To propose a hybrid technique for detecting rumor sources and predicting spread paths in social networks.
- To enhance the accuracy and efficiency of identifying influential users in rumor dissemination.
- To develop a method that combines deep learning with graph-based clustering for rumor analysis.
Main Methods:
- A two-phase algorithm: rumor detection and influential node identification.
- Utilizing a rumor spreading rate criterion and GloVe for content feature extraction.
- Employing deep neural networks for rumor classification and novel clustering for community detection.
Main Results:
- The proposed method effectively identifies rumor paths within social networks.
- Achieved an average accuracy of 99% in recognizing target data.
- Demonstrated a precision of 0.99 in identifying influential nodes spreading rumors.
Conclusions:
- The hybrid deep learning and graph clustering approach is highly effective for rumor source detection and spread path prediction.
- The method accurately identifies key influencers in rumor propagation.
- This technique offers a robust solution for combating misinformation on social media platforms.
