How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19

• URL: http://arxiv.org/abs/2204.07371v1
• Date: Fri, 15 Apr 2022 08:10:26 GMT
• Title: How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19
• Authors: Wenjie Yang, Zhiyang Wu, Nga Yiu Mok, Xiaojuan Ma
• Abstract summary: We analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo. We find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises.
• Score: 27.59580723175562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises.

Related papers

• NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants [79.27554831580309]
  We study how to build socially intelligent robots to assist people in their homes. We focus on assistance with online goal inference, where robots must simultaneously infer humans' goals.
  arXiv  Detail & Related papers  (2023-01-12T18:59:34Z)
• When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning [57.53138994155612]
  A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. We propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them.
  arXiv  Detail & Related papers  (2022-10-19T17:57:24Z)
• CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research [39.82073461647643]
  A substantial proportion of the population is still hesitant to be vaccinated against the COVID-19 virus. Existing datasets fail to cover all these aspects, making it difficult to form a complete picture in inferencing about the problem of vaccine hesitancy. In this paper, we construct a multi-source, multi-modal, and multi-feature online-offline data repository CoVaxNet.
  arXiv  Detail & Related papers  (2022-06-30T05:58:35Z)
• "Help! Can You Hear Me?": Understanding How Help-Seeking Posts are Overwhelmed on Social Media during a Natural Disaster [16.152052173909855]
  We collected 141,674 help-seeking posts with the keyword "Henan Rainstorm Mutual Aid" on a popular Chinese social media platform Weibo. We discover linguistic and non-linguistic help-seeking strategies that could help to prevent the overwhelm.
  arXiv  Detail & Related papers  (2022-05-25T07:14:58Z)
• Online discussion forums for monitoring the need for targeted psychological health support: an observational case study of r/COVID19_support [0.9558392439655015]
  The COVID-19 pandemic has placed a severe mental strain on people in general, and on young people in particular. Online support forums offer opportunities for peer-to-peer health support, which can ease pressure on professional and established volunteer services when demand is high. We created and monitored r/COVID19_support, an online forum for people seeking support during the COVID-19 pandemic, on the platform Reddit.
  arXiv  Detail & Related papers  (2022-01-25T18:58:35Z)
• Auto Response Generation in Online Medical Chat Services [0.0]
  We develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months.
  arXiv  Detail & Related papers  (2021-04-26T17:45:10Z)
• Clustering of Social Media Messages for Humanitarian Aid Response during Crisis [47.187609203210705]
  We show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness. We extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.
  arXiv  Detail & Related papers  (2020-07-23T02:18:05Z)
• Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions [1.6571886312953874]
  An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. This paper presents a large-scale study based on data mined from Twitter.
  arXiv  Detail & Related papers  (2020-05-18T15:53:13Z)
• Remote health monitoring and diagnosis in the time of COVID-19 [51.01158603315544]
  Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance.
  arXiv  Detail & Related papers  (2020-05-18T08:54:38Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.