Fusion of Emotional Information for Rumor Detection Model

Authors

  • Bai Li
  • Yujun Zhang

DOI:

https://doi.org/10.54097/jceim.v11i2.12461

Keywords:

Weibo Rumors, Sentiment Analysis, Deep Learning

Abstract

With the development of technology, the platforms through which people acquire information have shifted from traditional sources such as television and newspapers to today's social media platforms. However, due to the openness of social media platforms, it is challenging to ensure the quality of information. If false information is not addressed promptly, it can adversely affect people's daily lives and lead to social panic. Previous research has largely focused on textual semantic information, which has raised concerns about its limited generalization ability. To address this issue, this study utilized a microblog sentiment analysis dataset to train a sentiment feature extraction model. This trained model was then used for extracting emotional features related to microblog rumors through transfer learning. These emotional features were subsequently integrated with the extracted semantic information features. Experimental results demonstrate that the model achieved an accuracy of 96% on a publicly available rumor detection dataset.

References

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Qazvinian V, Rosengren E, Radev D, et al. Rumor has it: Identifying misinformation in microblogs[C]//Proceedings of the 2011 conference on empirical methods in natural language processing. 2011: 1589-1599.

Lizhao Li,Guoyong Cai,Jiao Pan. Microblog Rumor Event Detection Method Based on C-GRU(in Chinese) [J]. Journal of Shandong University (Engineering Edition),2019,49(02):102-106+115.

Ma J, Gao W, Wong K F. Detect rumor and stance jointly by neural multi-task learning[C]//Companion proceedings of the the web conference 2018. 2018: 585-593.

Ma J, Gao W, Mitra P, et al. Detecting rumors from microblogs with recurrent neural networks[J]. 2016.

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Published

06-10-2023

Issue

Section

Articles

How to Cite

Li, B., & Zhang, Y. (2023). Fusion of Emotional Information for Rumor Detection Model. Journal of Computing and Electronic Information Management, 11(2), 50-52. https://doi.org/10.54097/jceim.v11i2.12461

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