Abstract
Weibo (Chinese microblog) has become a popular social media platform for users to share health-related information. However, illegitimate users or spammers often generate and spread false or misleading health information so as to advertise and attract more attention. To address this issue, we propose a health-related spammer detection approach on Chinese social media. Our approach is a deep belief network (DBN) based model incorporating a comprehensive feature set, including burstiness-based features, profile-based features, and content-based features, to identify spammers who spread misleading health-related information. Especially, we create a medical and health domain lexicon to better extract content-based features. The experimental results show the approach achieves an F1 score of 86 % in detecting spammer and significantly outperforms the benchmark methods using baseline features.
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Acknowledgments
This work was supported by the National High-tech R&D Program of China (Grant No. SS2015AA020102), National Basic Research Program of China (Grant No. 2011CB302302), the 1000-Talent program, Tsinghua University Initiative Scientific Research Program.
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Chen, X., Zhang, Y., Xu, J., Xing, C., Chen, H. (2016). Health-Related Spammer Detection on Chinese Social Media. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_27
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DOI: https://doi.org/10.1007/978-3-319-29175-8_27
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