Abstract
At present, most of the rumor detection methods take the content of Weibo text as the main target of rumor detection. This study uses user information and Weibo text as the target to detect Weibo rumors, and the focus is on user information. A rumor detection model based on Bert [1] combined with DPCNN [2] method is proposed, which can process Chinese data more conveniently, extract the characteristics of user information more accurately, and introduce the evaluation standard as the final evaluation index. Finally, a microblog rumor detection system based on user information is constructed to make the rumor detection more accurate.
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Acknowledgements
This work is supported by National Nature Science Foundation of China-Xinjiang Joint Fund (U1703261) and Graduate Research Innovation Project (XJ2019-G231). Special thanks to Ms. Ma Jing, the Chinese University of Hong Kong for publishing the rumor detection data set.
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Li, YJ., Zhang, HJ., Pan, WM., Feng, RJ., Zhou, ZY. (2021). Microblog Rumor Detection Based on Bert-DPCNN. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z., Cai, X. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 653. Springer, Singapore. https://doi.org/10.1007/978-981-15-8599-9_60
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DOI: https://doi.org/10.1007/978-981-15-8599-9_60
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