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2021 | OriginalPaper | Buchkapitel

Syntax and Sentiment Enhanced BERT for Earliest Rumor Detection

verfasst von : Xin Miao, Dongning Rao, Zhihua Jiang

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Dual Adversarial Network Based on BERT for Cross-domain Sentiment Classification

Nächstes Kapitel Aspect-Sentiment-Multiple-Opinion Triplet Extraction

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http://service.account.weibo.com.

http://scikit-learn.org.

http://pytorch.org.

http://huggingface.co.

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Titel: Syntax and Sentiment Enhanced BERT for Earliest Rumor Detection
verfasst von: Xin Miao
Dongning Rao
Zhihua Jiang
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-88479-6

Electronic ISBN: 978-3-030-88480-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88480-2_45

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