• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Finding Users’ Voice on Social Media: An Investigation of Online Support Groups for Autism-Affected Users on Facebook
【2h】

Finding Users’ Voice on Social Media: An Investigation of Online Support Groups for Autism-Affected Users on Facebook

机译：在社交媒体上寻找用户的声音：对Facebook上自闭症受累用户的在线支持小组的调查

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The trend towards the use of the Internet for health information purposes is rising. Utilization of various forms of social media has been a key interest in consumer health informatics (CHI). To reveal the information needs of autism-affected users, this study centers on the research of users’ interactions and information sharing within autism communities on social media. It aims to understand how autism-affected users utilize support groups on Facebook by applying natural language process (NLP) techniques to unstructured health data in social media. An interactive visualization method (pyLDAvis) was employed to evaluate produced models and visualize the inter-topic distance maps. The revealed topics (e.g., parenting, education, behavior traits) identify issues that individuals with autism were concerned about on a daily basis and how they addressed such concerns in the form of group communication. In addition to general social support, disease-specific information, collective coping strategies, and emotional support were provided as well by group members based on similar personal experiences. This study concluded that Latent Dirichlet Allocation (LDA) is feasible and appropriated to derive topics (focus) from messages posted to the autism support groups on Facebook. The revealed topics help healthcare professionals (content providers) understand autism from users’ perspectives and provide better patient communications.
机译：将互联网用于健康信息的趋势正在上升。各种形式的社交媒体的使用一直是消费者健康信息学（CHI）的主要兴趣。为了揭示受自闭症影响的用户的信息需求，本研究重点研究了社​​交媒体上自闭症社区内用户的交互和信息共享。它旨在通过将自然语言处理（NLP）技术应用于社交媒体中非结构化的健康数据，来了解受自闭症影响的用户如何利用Facebook上的支持小组。交互式可视化方法（pyLDAvis）用于评估生产的模型并可视化主题间距离图。所揭示的主题（例如，养育，教育，行为特征）确定了自闭症患者日常关注的问题，以及他们如何以小组沟通的方式解决此类问题。除了一般的社会支持外，小组成员还根据相似的个人经历提供了针对疾病的信息，集体应对策略和情感支持。这项研究的结论是，潜在的狄利克雷分配（LDA）是可行的，适合从Facebook上自闭症支持小组发布的消息中得出主题（焦点）。揭示的主题可帮助医疗保健专业人员（内容提供商）从用户的角度理解自闭症，并提供更好的患者沟通。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号