Abstract
With the rapid development of artificial intelligence technology, people's communication become more frequent, enjoying convenient at the same time, also aggravated the spread of rumors and spread. Therefore, rumor detection in social platforms has become an important direction of current scientific research. From the perspective of User characteristics, this paper uses deep learning methods to mine the change trend of user characteristics related to rumor events, and designs a rumor detection Model (User Feature Information Model, UFIM). Firstly, the feature enhancement function is used to recalculate the user feature vector to obtain a new feature vector representing the user's comprehensive feature under the current event. Then, the GRU model and the CNN model are used to learn the global and local changes of user features with the development of the event, and the user and time information are used to learn the hidden rumor features in the process of rumor spreading. The experimental results show that the UFIM model improved performance compared with the baseline model, rumors can effectively realize detection task.
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This work is partly supported by “the Fundamental Research Funds for the Central Universities CUC230A013”.
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Shang, W., Song, K., Zhang, Y., Yi, T., Wang, X. (2024). A Rumor Detection Model Fused with User Feature Information. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_13
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DOI: https://doi.org/10.1007/978-981-99-9893-7_13
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