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Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events

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Listed:
  • Guan, Wanqiu
  • Gao, Haoyu
  • Yang, Mingmin
  • Li, Yuan
  • Ma, Haixin
  • Qian, Weining
  • Cao, Zhigang
  • Yang, Xiaoguang

Abstract

The spread and resonance of users’ opinions on Sina Weibo, the most popular micro-blogging website in China, are tremendously influential, having significantly affected the processes of many real-world hot social events. We select 21 hot events that were widely discussed on Sina Weibo in 2011, and do some statistical analyses. Our main findings are that (i) male users are more likely to be involved, (ii) messages that contain pictures and those posted by verified users are more likely to be reposted, while those with URLs are less likely, (iii) the gender factor, for most events, presents no significant difference in reposting likelihood.

Suggested Citation

  • Guan, Wanqiu & Gao, Haoyu & Yang, Mingmin & Li, Yuan & Ma, Haixin & Qian, Weining & Cao, Zhigang & Yang, Xiaoguang, 2014. "Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 340-351.
    • Handle: RePEc:eee:phsmap:v:395:y:2014:i:c:p:340-351
    DOI: 10.1016/j.physa.2013.09.059
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    References listed on IDEAS

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    1. Boris Podobnik & Davor Horvatic & Dror Y. Kenett & H. Eugene Stanley, 2012. "The competitiveness versus the wealth of a country," Papers 1209.2813, arXiv.org.
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    Cited by:

    1. Li, Lifang & Zhang, Qingpeng & Tian, Jun & Wang, Haolin, 2018. "Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake," International Journal of Information Management, Elsevier, vol. 38(1), pages 34-41.
    2. Jia Tao & Meng Yang & Jing Wu, 2022. "Coupling Coordination Evaluation of Lakefront Landscape Spatial Quality and Public Sentiment," Land, MDPI, vol. 11(6), pages 1-29, June.
    3. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    4. Wang, Xu & Baesens, Bart & Zhu, Zhen, 2019. "On the optimal marketing aggressiveness level of C2C sellers in social media: Evidence from china," Omega, Elsevier, vol. 85(C), pages 83-93.
    5. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.
    6. Zhan Liu & Jialu Shan & Nicole Glassey Balet & Gang Fang, 0. "Semantic social media analysis of Chinese tourists in Switzerland," Information Technology & Tourism, Springer, vol. 0, pages 1-20.
    7. Zhan Liu & Jialu Shan & Nicole Glassey Balet & Gang Fang, 2017. "Semantic social media analysis of Chinese tourists in Switzerland," Information Technology & Tourism, Springer, vol. 17(2), pages 183-202, June.
    8. Xiaoxue Cheng & Guifeng Han & Yifan Zhao & Lin Li, 2019. "Evaluating Social Media Response to Urban Flood Disaster: Case Study on an East Asian City (Wuhan, China)," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    9. Liu, Yu & Wang, Bai & Wu, Bin & Shang, Suiming & Zhang, Yunlei & Shi, Chuan, 2016. "Characterizing super-spreading in microblog: An epidemic-based information propagation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 202-218.
    10. Jiayin Pei & Zhi Lu & Xiaoming Yang, 2022. "What drives people to repost social media messages during the COVID‐19 pandemic? Evidence from the Weibo news microblog," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1609-1626, December.
    11. Rizwan Muhammad & Yaolong Zhao & Fan Liu, 2019. "Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 11(10), pages 1-30, May.
    12. Eunbee Gil & Yongjin Ahn & Youngsang Kwon, 2020. "Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    13. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    14. Qing Deng & Yi Liu & Hui Zhang & Xiaolong Deng & Yefeng Ma, 2016. "A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 1241-1256, November.
    15. Li, Yuan & Gao, Haoyu & Yang, Mingmin & Guan, Wanqiu & Ma, Haixin & Qian, Weining & Cao, Zhigang & Yang, Xiaoguang, 2015. "What are Chinese talking about in hot weibos?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 546-557.

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