An Examination of Users' Influence in Online HIV/AIDS Communities

A network perspective was adopted in this study to identify influential users in an online HIV community in China. Specifically, the indegree centrality, outdegree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient of individuals were evaluated to measure the user influence in such a community. Moreover, this study examined how the digital divide, which is presently one of the major social equity issues in the information society, is associated with an individual's influence within the community. Two networks were formed on the basis of the behavioral data retrieved from the HIV community: the follower-followee network and the post-reply network. In the follower-followee network, members from areas with well-developed technologies demonstrated more connections, received more attention, and secured more critical positions in the network than their counterparts. However, such differences were insignificant in the post-reply network.

[1]  Rod Carveth,et al.  The Digital Divide in Western Europe: Problems and Prospects , 2002 .

[2]  Dylan Walker,et al.  Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment , 2014, Manag. Sci..

[3]  Patti Collett Miles,et al.  China Turns to Tweeting: Exploring the Problematic use of Tweeting in China , 2012 .

[4]  Keith F Widaman,et al.  HIV-related stigma and knowledge in the United States: prevalence and trends, 1991-1999. , 2002, American journal of public health.

[5]  B. Wellman.  Computer Networks As Social Networks , 2001, Science.

[6]  D. Reidpath,et al.  Institutional and structural forms of HIV-related discrimination in health care: A study set in Beijing , 2005, AIDS care.

[7]  Chao-Hsien Hsieh,et al.  Effects of Internet Popular Opinion Leaders (iPOL) Among Internet-Using Men Who Have Sex With Men , 2013, Journal of medical Internet research.

[8]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[9]  John P. Robinson,et al.  Social Implications of the Internet , 2001 .

[10]  Thomas W. Valente,et al.  Social Networks and Health: Models, Methods, and Applications , 2010 .

[11]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[12]  Jingyuan Shi,et al.  The Use of Social Networking Sites (SNSs) in Health Communication Campaigns: Review and Recommendations , 2018, Health communication.

[13]  Charles K. Atkin,et al.  Persuasive Strategies in Health Campaigns , 2012 .

[14]  Kathleen M. Carley,et al.  Patterns and dynamics of users' behavior and interaction: Network analysis of an online community , 2009 .

[15]  Mark Batey,et al.  A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage , 2012, Comput. Hum. Behav..

[16]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[17]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[18]  Hui Gao,et al.  Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering , 2013, PloS one.

[19]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[20]  T. Coates,et al.  Project HOPE: online social network changes in an HIV prevention randomized controlled trial for African American and Latino men who have sex with men. , 2014, American journal of public health.

[21]  Liang Chen,et al.  Social support on Weibo for people living with HIV/AIDS in China: a quantitative content analysis , 2014 .

[22]  Edward L. Fink,et al.  Understanding the Influential People and Social Structures Shaping Compliance , 2015, J. Soc. Struct..

[23]  Alex Bavelas,et al.  Communication Patterns in Task‐Oriented Groups , 1950 .

[24]  Ronald S. Burt,et al.  Embedded Brokerage: Hubs Versus Locals , 2014 .

[25]  Martin G. Everett,et al.  Analyzing social networks , 2013 .

[26]  Weidong Liu,et al.  Measuring spatial differences of informatization in China , 2014, Chinese Geographical Science.

[27]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[28]  Ali Acilar,et al.  Issues in Informing Science and Information Technology Exploring the Aspects of Digital Divide in a Developing Country , 2022 .

[29]  P. Norris.  Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide , 2001 .

[30]  Thomas W. Valente,et al.  Social Networks and Health , 2010 .

[31]  Robert LaRose,et al.  Internet Self-Efficacy and the Psychology of the Digital Divide , 2006, J. Comput. Mediat. Commun..

[32]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[33]  Hayder Radha,et al.  Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality , 2011, 2011 IEEE International Conference on Communications (ICC).

[34]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[35]  R. Dellavalle,et al.  Social internet sites as a source of public health information. , 2009, Dermatologic clinics.

[36]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[37]  D. Capurro,et al.  The Use of Social Networking Sites for Public Health Practice and Research: A Systematic Review , 2014, Journal of medical Internet research.