Identifying HIV-related digital social influencers using an iterative deep learning approach.

Journal: AIDS (London, England)
Published Date: May 1, 2021

Abstract

OBJECTIVES: Community popular opinion leaders have played a critical role in HIV prevention interventions. However, it is often difficult to identify these 'HIV influencers' who are qualified and willing to promote HIV campaigns, especially online, because social media influencers change frequently. We sought to use an iterative deep learning framework to automatically discover HIV-related online social influencers.

Authors

  • Cheng Zheng
    Department of Computer Science, University of California, Los Angeles.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Sean D Young
    *Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA; †University of California Institute for Prediction Technology, University of California, Los Angeles, CA; and ‡Department of Computer Science, University of California, Los Angeles, CA.

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