Open AccessIdentifying HIV-related digital social influencers using an iterative deep learning approach
Author(s) -
Cheng Zheng,
Wei Wang,
Sean D. Young
Publication year - 2021
Publication title -
aids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.195
H-Index - 216
eISSN - 1473-5571
pISSN - 0269-9370
DOI - 10.1097/qad.0000000000002841
Subject(s) - influencer marketing , social media , computer science , conversation , recall , psychological intervention , baseline (sea) , artificial intelligence , machine learning , precision and recall , deep learning , medicine , world wide web , psychology , communication , business , oceanography , marketing , psychiatry , relationship marketing , cognitive psychology , marketing management , geology
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.