"help! Can You Hear Me?": Understanding How Help-Seeking Posts are Overwhelmed on Social Media during a Natural Disaster

被引:4
作者
He C. [1 ]
Deng Y. [1 ]
Yang W. [1 ]
Li B. [1 ]
机构
[1] Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR
关键词
crisis communication; online community; public engagement; seeking help; social media;
D O I
10.1145/3555147
中图分类号
学科分类号
摘要
Posting help-seeking requests on social media has been broadly adopted by victims during natural disasters to look for urgent rescue and supplies. The help-seeking requests need to get sufficient public attention and be promptly routed to the intended target(s) for timely responses. However, the huge volume and diverse types of crisis-related posts on social media might limit help-seeking requests to receive adequate engagement and lead to their overwhelm. To understand this problem, this work proposes a mixed-methods approach to figure out the overwhelm situation of help-seeking requests, and individuals' and online communities' strategies to cope. We focused on the 2021 Henan Floods in China and collected 141,674 help-seeking posts with the keyword "Henan Rainstorm Mutual Aid"on a popular Chinese social media platform Weibo. The findings indicate that help-seeking posts confront critical challenges of both external overwhelm (i.e., an enormous number of non-help-seeking posts with the help-seeking-related keyword distracting public attention) and internal overwhelm (i.e., attention inequality with 5% help-seeking posts receiving more than 95% likes, comments, and shares). We discover linguistic and non-linguistic help-seeking strategies that could help to prevent the overwhelm, such as including contact information, disclosing situational vulnerabilities, using subjective narratives, and structuring help-seeking posts to a normalized syntax. We also illustrate how community members spontaneously work to prevent the overwhelm with their collective wisdom (e.g., norm development through discussion) and collaborative work (e.g., cross-community support). We reflect on how the findings enrich the literature in crisis informatics and raise design implications that facilitate effective help-seeking on social media during natural disasters. © 2022 ACM.
引用
收藏
相关论文
共 101 条
[1]  
Abdollahpouri H., Popularity bias in ranking and recommendation, Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 529-530, (2019)
[2]  
Alexander D., Natural Disasters, (2018)
[3]  
Arif A., Robinson J.J., Stanek S.A., Fichet E.S., Townsend P., Worku Z., Starbird K., A closer look at the self-correcting crowd: Examining corrections in online rumors, Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 155-168, (2017)
[4]  
Austin L.L., Jin Y., Social Media and Crisis Communication, (2018)
[5]  
Baeza-Yates R., 2018 Bias on the web, Commun ACM, 61, 6, pp. 54-61, (2018)
[6]  
Baike B., 2021 Henan Floods (Chinese), (2021)
[7]  
Berger J., Milkman K.L., 2012 What makes online content viral?, Journal of Marketing Research, 49, 2, pp. 192-205, (2012)
[8]  
Jo Brubaker P., Wilson C., 2018 Let?s give them something to talk about: Global brands? use of visual content to drive engagement and build relationships, Public Relations Review, 44, 3, pp. 342-352, (2018)
[9]  
Caulfield T., Does Debunking Work? Correcting COVID-19 Misinformation on Social Media, 2020, (2020)
[10]  
Chancellor S., Hu A., De Choudhury M., Norms matter: Contrasting social support around behavior change in online weight loss communities, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-14, (2018)