title: Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives.
abstract: BACKGROUND
  Mental health problems have become increasingly prevalent in the past decade. With the advance of Web 2.0 technologies, social media present a novel platform for Web users to form online health groups. Members of online health groups discuss health-related issues and mutually help one another by anonymously revealing their mental conditions, sharing personal experiences, exchanging health information, and providing suggestions and support. The conversations in online health groups contain valuable information to facilitate the understanding of their mutual help behaviors and their mental health problems.
  
  
  OBJECTIVE
  We aimed to characterize the conversations in a major online health group for major depressive disorder (MDD) patients in a popular Chinese social media platform. In particular, we intended to explain how Web users discuss depression-related issues from the perspective of the social networks and linguistic patterns revealed by the members' conversations.
  
  
  METHODS
  Social network analysis and linguistic analysis were employed to characterize the social structure and linguistic patterns, respectively. Furthermore, we integrated both perspectives to exploit the hidden relations between them.
  
  
  RESULTS
  We found an intensive use of self-focus words and negative affect words. In general, group members used a higher proportion of negative affect words than positive affect words. The social network of the MDD group for depression possessed small-world and scale-free properties, with a much higher reciprocity ratio and clustering coefficient value as compared to the networks of other social media platforms and classic network models. We observed a number of interesting relationships, either strong correlations or convergent trends, between the topological properties and linguistic properties of the MDD group members.
  
  
  CONCLUSIONS
  (1) The MDD group members have the characteristics of self-preoccupation and negative thought content, according to Beck's cognitive theory of depression; (2) the social structure of the MDD group is much stickier than those of other social media groups, indicating the tendency of mutual communications and efficient spread of information in the MDD group; and (3) the linguistic patterns of MDD members are associated with their topological positions in the social network.
authors.affiliation: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, China (Hong Kong).
authors.forename: Ronghua
authors.identifier: http://orcid.org/0000-0001-7349-1005
authors.initials: R
authors.lastname: Xu
authors.forename: Qingpeng
authors.identifier: http://orcid.org/0000-0002-6819-0686
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D000328:Adult; D002681:China; D016000:Cluster Analysis; D003865:Major Depressive Disorder; D005260:Female; D006801:Humans; D020407:Internet; D008037:Linguistics; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012919:Social Behavior; D061108:Social Media; D060756:Social Networking; D012944:Social Support
keywords: depression; information science; mental health; online health group; social media; social network analysis