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Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology

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2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 102))

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Abstract

This study based on big data collection technology with Weibo contents to reveal the relationship between negative emotion and information diffusion during Covid-19 pandemic. Specifically, focusing on how negative emotion influences the number of reposts (retweets). From January 23 to February 7 2020, 176,934 Weibo posts collected with the keyword “novel coronavirus pneumonia”. Negative binomial regression method is applied to construct an empirical model between negative emotion and retweets. Regression results demonstrated that there is not a single linear relationship between the two, when the negative emotion exceed a certain level, retweets would decrease instead. Our results implicate risk communication can be manipulated by controlling the negative intensity in social media contents, even under extreme risk.

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Author information

Authors and Affiliations

  1. School of Advertising, Communication University of China, Beijing, China

    Xinmiao Zhang

Authors
  1. Xinmiao Zhang
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Correspondence to Xinmiao Zhang .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

  2. University of Aizu, Aizuwakamatsu, Japan

    Neil Yen

  3. Shanghai Polytechnic University, Shanghai, China

    Zheng Xu

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, X. (2022). Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 102. Springer, Singapore. https://doi.org/10.1007/978-981-16-7466-2_36

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