Sunbelt XXXI International Network for Social Network ... - INSNA

Social Network Effects On Sexually Risky And Exploitative Behaviors In Street Youth In San Francisco Differ By GenderValente, Annie M.; Auerswald, Colette L.Social Networks and HealthAdolescents, HIV Risk, Gender, Sex Networks, HomelessSAT.AM1Homeless youth have unique social environments contributing to the development of high‐risk behaviors and poor health outcomes. We examine hownetwork structure (degree and measures of subgroup formation, n‐cliques and k‐plexes) and sense of network support relate to risky and exploitative sexualbehaviors, and how these correlations vary by gender. We interviewed 266 venue‐recruited youth about their networks, behaviors, and street beliefs, andconducted follow‐up interviews with 138 respondents, collecting inter‐alter ties at that time. Alter identities were matched, generating sociometric data. Wecompared degree, ranked k‐plex/n‐cliques, and support scores with behavioral outcomes, by gender. Our sample included 167 (63%) males and 92 (35%)females. Mean network size was 5.8 for males and 5.7 for females. For young women, increased degree and support are associated with more sex partners inthe last 3 months, higher likelihood of sex with an HIV‐positive partner, and increased pimping. For young men, increased degree is associated with decreasedpimping, and increased network support is associated with decreased sex partners and pimping. While larger networks and sense of community may beprotective for young men, they seem to be harmful for young women. Knowledge regarding gender‐specific network effects may inform interventions forhomeless youth.Social Network Measures As Semantic Text Analysis Indicators For Compound TokensElbirt, Benjamin S.Words and Networks ‐ Roles, Health, MethodsMethods, Longitudinal, Semantic Networks, Content Analysis, Visual AnalyticsFRI.PM1This presentation describes methodology for using degree centrality to join tokens (words) into clusters that better represent the data during a co‐occurrencesemantic network analysis. The method uses both binary and strength based degree centrality to identify outlier tokens. These tokens are then combinedwith related tokens to form new token‐clusters. Results indicate a reduction in deviations, outliers and token volatility which in turn creates a better analysis.The presentation will start by introducing the data set and general methodology for semantic analysis. This is followed by a presentation of the new methodsfor token clustering. Next, the final token list with and without the new methodology added are provided with 3D OpenGL visualizations. Finally, discussion ispresented regarding the results and their implication on future semantic analysis.