title: A Multirelational Social Network Analysis of an Online Health Community for Smoking Cessation.
abstract: Online health communities (OHCs) provide a convenient and commonly used way for people to connect around shared health experiences, exchange information, and receive social support. Users often interact with peers via multiple communication methods, forming a multirelational social network. Use of OHCs is common among smokers, but to date, there have been no studies on users' online interactions via different means of online communications and how such interactions are related to smoking cessation. Such information can be retrieved in multirelational social networks and could be useful in the design and management of OHCs.
  
  
  
  To examine the social network structure of an OHC for smoking cessation using a multirelational approach, and to explore links between subnetwork position (ie, centrality) and smoking abstinence.
  
  
  
  We used NetworkX to construct 4 subnetworks based on users' interactions via blogs, group discussions, message boards, and private messages. We illustrated topological properties of each subnetwork, including its degree distribution, density, and connectedness, and compared similarities among these subnetworks by correlating node centrality and measuring edge overlap. We also investigated coevolution dynamics of this multirelational network by analyzing tie formation sequences across subnetworks. In a subset of users who participated in a randomized, smoking cessation treatment trial, we conducted user profiling based on users' centralities in the 4 subnetworks and identified user groups using clustering techniques. We further examined 30-day smoking abstinence at 3 months postenrollment in relation to users' centralities in the 4 subnetworks.
  
  
  
  The 4 subnetworks have different topological characteristics, with message board having the most nodes (36,536) and group discussion having the highest network density (4.35×10(-3)). Blog and message board subnetworks had the most similar structures with an in-degree correlation of .45, out-degree correlation of .55, and Jaccard coefficient of .23 for edge overlap. A new tie in the group discussion subnetwork had the lowest probability of triggering subsequent ties among the same two users in other subnetworks: 6.33% (54,142/855,893) for 2-tie sequences and 2.13% (18,207/855,893) for 3-tie sequences. Users' centralities varied across the 4 subnetworks. Among a subset of users enrolled in a randomized trial, those with higher centralities across subnetworks generally had higher abstinence rates, although high centrality in the group discussion subnetwork was not associated with higher abstinence rates.
  
  
  
  A multirelational approach revealed insights that could not be obtained by analyzing the aggregated network alone, such as the ineffectiveness of group discussions in triggering social ties of other types, the advantage of blogs, message boards, and private messages in leading to subsequent social ties of other types, and the weak connection between one's centrality in the group discussion subnetwork and smoking abstinence. These insights have implications for the design and management of online social networks for smoking cessation.
authors: Department of Management Sciences, Tippie College of Business, The University of Iowa, Iowa City, IA, United States. kang-zhao@uiowa.edu.
authors: Kang
authors: 0000-0002-8321-2804
authors: K
authors: Zhao
authors: Xi
authors: 0000-0002-7568-0851
authors: X
authors: Wang
authors: Sarah
authors: 0000-0003-3505-3164
authors: S
authors: Cha
authors: Amy M
authors: 0000-0001-9034-4293
authors: AM
authors: Cohn
authors: George D
authors: 0000-0001-6770-932X
authors: GD
authors: Papandonatos
authors: Michael S
authors: 0000-0002-9769-957X
authors: MS
authors: Amato
authors: Jennifer L
authors: 0000-0002-1400-5932
authors: JL
authors: Pearson
authors: Amanda L
authors: 0000-0003-3036-9653
authors: AL
authors: Graham
mesh_terms: D006801:Humans; D020407:Internet; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D016540:Smoking Cessation / Q000379:methods* / Q000523:psychology; D060756:Social Networking; D012944:Social Support; D017216:Telemedicine / Q000379:methods*; D013813:Therapy, Computer-Assisted / Q000379:methods*
keywords: community networks; smoking cessation; social networks