Yi-Chia盟泰-非盟的Wang -德国人,罗伯特E AU -莱文,约翰M PY - 2015 DA - 2015/04/20 TI -诱发和接收在线支持:使用计算机辅助内容分析研究的动态在线社会支持乔- J地中海互联网Res SP - e99六世- 17 - 4 KW -社会支持KW -健康传播KW -自我表露KW -社会媒体KW -支持团体KW -情绪KW -自然语言处理AB -背景:尽管许多患有严重疾病的人参加在线支持社区，但很少有研究调查参与者如何在这些网站上获得和提供社会支持。目的:第一个目标是提出并测试一个动态过程的模型，通过该模型，在线支持社区的参与者引出并提供情感和信息支持。第二项是使用机器学习技术演示对话数据的计算机编码的价值(1)通过复制关于人们如何获得支持的人类编码数据的结果;(2)通过回答人类编码数据的小样本难以解决的问题，即暴露于不同类型的社会支持如何预测在线支持社区的持续参与。第三是提供这些机器学习技术的详细描述，以使其他研究人员能够在这些社区中进行大规模的数据分析。方法:分析了在线癌症支持社区约9万名注册用户的交流情况。该语料库包含1,562,459条消息，分为68,158个讨论线程。亚马逊机械土耳其工人编码了(1)1000条关于5个属性的线程启动消息(积极和消极的情感自我表露，积极和消极的信息自我表露，问题)和(2)1000条关于情感和信息支持的回复。他们的判断被用来训练机器学习模型，自动估计消息中这7个属性的数量。在属性方面，基于人类的判断和基于计算机的判断之间的平均皮尔逊相关系数为0.65。 Results: Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=–.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=–.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Conclusions: Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities. SN - 1438-8871 UR - //www.mybigtv.com/2015/4/e99/ UR - https://doi.org/10.2196/jmir.3558 UR - http://www.ncbi.nlm.nih.gov/pubmed/25896033 DO - 10.2196/jmir.3558 ID - info:doi/10.2196/jmir.3558 ER -