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Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China

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  • Lian, Ying
  • Liu, Yijun
  • Dong, Xuefan

Abstract

On September 16, 2018, Typhoon Mangkhut, with record sustained winds of an estimated 110–115 mph, struck southern China, causing tremendous damage to the region. Since then, there have been many online discussions about the typhoon and how the government responded to it. In the domain of emergency response to disasters, the increasingly prominent role of social websites has been confirmed. This paper presents a case study of public attitudes and opinions regarding Typhoon Mangkhut that were collected from the Sina Weibo and WeChat websites, as well as from news websites. We then present a method for managing false online information in the wake of such natural disasters. The method consists of four phases: information extraction, sentiment recognition, opinion classification, and simulation analysis. In the last phase, a simulation experiment was conducted to assess how false information spreads. We used three scenarios in the simulation—namely, a real scenario, an isolation scenario, and an embedment scenario, each of which was based on a scale-free network based on a scale-free network. The results showed that the embedment strategy better controlled the transmission of false information than did the isolation strategy. In addition, we have provided some practical suggestions. This paper contributes to the literature on the emergency management of natural disasters.

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  • Lian, Ying & Liu, Yijun & Dong, Xuefan, 2020. "Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China," Technology in Society, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:teinso:v:62:y:2020:i:c:s0160791x1930569x
    DOI: 10.1016/j.techsoc.2020.101265
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    2. Yang, Zaoli & Wu, Qingyang & Venkatachalam, K. & Li, Yuchen & Xu, Bing & Trojovský, Pavel, 2022. "Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Raj, Chahat & Meel, Priyanka, 2022. "People lie, actions Don't! Modeling infodemic proliferation predictors among social media users," Technology in Society, Elsevier, vol. 68(C).
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    5. Balakrishnan, Vimala & Ng, Kee S. & Rahim, Hajar Abdul, 2021. "To share or not to share – The underlying motives of sharing fake news amidst the COVID-19 pandemic in Malaysia," Technology in Society, Elsevier, vol. 66(C).
    6. Pang, Hua & Liu, Jun & Lu, Jiahui, 2022. "Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat," Technology in Society, Elsevier, vol. 70(C).
    7. Turgut Acikara & Bo Xia & Tan Yigitcanlar & Carol Hon, 2023. "Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature," Sustainability, MDPI, vol. 15(11), pages 1-50, May.
    8. Hou, Shengjie & Zhang, Xiang & Yi, Biyi & Tang, Yi, 2022. "Public attitudes on open source communities in China: A text mining analysis," Technology in Society, Elsevier, vol. 71(C).
    9. Liu, Liyi & Tu, Yan & Zhou, Xiaoyang, 2022. "How local outbreak of COVID-19 affect the risk of internet public opinion: A Chinese social media case study," Technology in Society, Elsevier, vol. 71(C).
    10. Zha, Wenbin & Ye, Qian & Li, Jian & Ozbay, Kaan, 2023. "A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).

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