Yang Diyi, Yao Zheng, Kraut Robert
Language Technologies Institute, Carnegie Mellon University.
Human-Computer Interaction Institute, Carnegie Mellon University.
Proc Int AAAI Conf Weblogs Soc Media. 2017 May;2017:704-707.
Online health support groups are places for people to compare themselves with others and obtain informational and emotional support about their disease. To do so, they generally need to reveal private information about themselves and in many support sites, they can do this in public or private channels. However, we know little about how the publicness of the channels in health support groups influence the amount of self-disclosure people provide. Our work examines the extent members self-disclose in the private and public channels of an online cancer support group. We first built machine learning models to automatically identify the amount of positive and negative self-disclosure in messages exchanged in this community, with adequate validity (r>0.70). In contrast to findings from non-health-related sites, our results show that people generally self-disclose more in the public channel than the private one and are especially likely to reveal their negative thoughts and feelings publicly. We discuss theoretical and practical implications of our work.
在线健康支持小组是人们将自己与他人进行比较，并获取有关自身疾病的信息和情感支持的场所。为此，他们通常需要披露自己的私人信息，并且在许多支持网站上，他们可以通过公共或私人渠道来做到这一点。然而，我们对健康支持小组中渠道的公开程度如何影响人们自我披露的信息量知之甚少。我们的研究考察了在线癌症支持小组的成员在私人和公共渠道中自我披露的程度。我们首先构建了机器学习模型，以自动识别在这个社区中交换的信息中积极和消极自我披露的数量，其有效性良好（r>0.70）。与非健康相关网站的研究结果相反，我们的结果表明，人们通常在公共渠道中比在私人渠道中自我披露得更多，并且特别有可能在公共场合透露他们的消极想法和感受。我们讨论了我们研究工作的理论和实际意义。
