@Article{信息:doi 10.2196 / / jmir。3558，作者="王懿家和Kraut, Robert E和Levine, John M"，题目="网络支持的诱导与接受:基于计算机辅助内容分析的网络社会支持动态研究"，期刊="J Med Internet Res"，年="2015"，月="4"，日="20"，卷="17"，数="4"，页数="e99"，关键词="社会支持;健康的沟通;自我表露;社交媒体;支持团体;情绪;背景:尽管许多患有严重疾病的人参与在线支持社区，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。 The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Methods: Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .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. ", issn="1438-8871", doi="10.2196/jmir.3558", url="//www.mybigtv.com/2015/4/e99/", url="https://doi.org/10.2196/jmir.3558", url="http://www.ncbi.nlm.nih.gov/pubmed/25896033" }