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Feature Analysis of Rumor Refuting Message Commentators on Social Media

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1190)

Abstract

In recent years, social networking platforms have rapidly become an important source of rumor propagation. The existence of internet rumors not only disturbs the normal thinking of the public, but also affects the security and stability of the society. This paper makes a sentiment analysis of comments on rumor refuting microblogs, to analyze the emotional tendency and characteristics of commentators from five main rumor categories: economics, society, military, politics and disaster. It finds out the factors that influence Weibo users’ positive and negative emotional tendencies. The paper shows there is a positive correlation between the emotional tendency of microblog contents and users’ emotional tendency to comments, except military category. Meanwhile, features such as gender, age, user labels, location and sentiment tendency also varies between commentators considering categories. Therefore, personalized recommendation services can be provided to target groups to trigger the spread of the truth, reduce the impact of rumors.

Keywords

  • Rumor refute
  • Commentators
  • Sentiment analysis
  • Feature analysis

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Acknowledgements

This research was supported by the National Natural Science Foundations (Grant No. 71601134), China Postdoctoral Science Foundation (Grant No. 2017M612983), Chengdu Philosophy and Social Science Planing Project (Grant No. 2019L40) and Basic Scientific Research Operating Expenses of Central Universities from Sichuan University (Grant No. 2019 Ziyan-Shangxue C01).

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Correspondence to Zongmin Li .

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Liu, X., Li, Z. (2020). Feature Analysis of Rumor Refuting Message Commentators on Social Media. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_7

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