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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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Using Partially-Observed Facebook Networks to Develop a Peer-Based HIV Prevention Intervention: Case Study.

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Summary

Peer network interventions using Facebook data can help prevent HIV among young Black men who have sex with men. Eigenvector centrality is a reliable method for identifying key influencers in these social networks.

Keywords:
African AmericansHIV infectionscomputer simulationdata miningpeer grouppre-exposure prophylaxissexual and gender minoritiessocial mediasocial networking

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Area of Science:

  • Social network analysis
  • Public health interventions
  • Digital health

Background:

  • Individual-level HIV prevention has limited success in young Black men who have sex with men.
  • Peer network-based interventions show promise for this disproportionately affected population.
  • Facebook offers a platform for social network analysis but presents challenges for intervention design.

Observation:

  • A study used Facebook data from 298 respondents and over 180,000 nonrespondent friends.
  • Relational boundaries and stochastic imputation (exponential random graph models) addressed network size and partial data.
  • Eigenvector centrality and keyplayer algorithms identified potential peer change agents.

Findings:

  • Eigenvector centrality consistently identified peer change agents across imputed networks and was less sensitive to boundary specifications.
  • The keyplayer algorithm showed lower agreement between observed and imputed networks.
  • Eigenvector centrality appears more stable and reliable for identifying influencers in partially observed Facebook networks.

Implications:

  • The study presents methods to overcome challenges in using Facebook data for peer interventions.
  • Eigenvector centrality may be a preferable method for identifying key change agents in online social networks.
  • These findings support the use of online social networks for improving population health interventions.