Abstract
With the rapid development of social media, rumor is becoming an increasingly significant problem. Although quite a few researches have been proposed recently, most of methods rely on contextual information or propagation pattern of reply posts. For some threatening rumors, we need to interrupt their transmission in the beginning. To solve this problem, we propose Syntax and Sentiment Enhanced BERT (SSE-BERT), which can achieve superior performance only based on source post. SSE-BERT can learn extra syntax and sentiment features by additional linguistic knowledge. Experimental results on two real-word datasets show that our method outperforms some state-of-the-art methods on earliest rumor detection. Furthermore, to alleviate the shortage of Chinese dataset, we collect a new rumor detection dataset Weibo20 (The experimental resource is available https://github.com/SeanMiao95/SSE-BERT).
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Acknowledgement
We would like to thank the anonymous reviewers for their valuable comments. This work was supported by Guangdong Basic and Applied Basic Research Foundation [grant number 2021A1515012556].
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Miao, X., Rao, D., Jiang, Z. (2021). Syntax and Sentiment Enhanced BERT for Earliest Rumor Detection. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_45
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DOI: https://doi.org/10.1007/978-3-030-88480-2_45
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