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http://dx.doi.org/10.1111/jocd.70151 | DOI Listing |
J Med Internet Res
September 2025
Department of Psychological and Brain Sciences, Boston University, Boston, United States.
Background: Lesbian, gay, bisexual, transgender, queer/questioning, intersex, asexual (LGBTQIA+) researchers and participants frequently encounter hostility in virtual environments, particularly on social media platforms where public commentary on research advertisements can foster stigmatization. Despite a growing body of work on researcher virtual hostility, little empirical research has examined the actual content and emotional tone of public responses to LGBTQIA+-focused research recruitment.
Objective: This study aimed to analyze the thematic patterns and sentiment of social media comments directed at LGBTQIA+ research recruitment advertisements, in order to better understand how virtual stigma is communicated and how it may impact both researchers and potential participants.
Dig Liver Dis
September 2025
School of Medicine, Nankai University, 94 Weijin Road, Tianjin, 300071, China. Electronic address:
Int J Oral Maxillofac Surg
September 2025
Department of Biostatistics, Faculty of Medicine, Hacettepe University, Sıhhiye, Ankara, Turkey.
Diabetes Res Clin Pract
September 2025
Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan. Electronic address:
Acta Psychol (Amst)
September 2025
Management Department, Faculty of Economics, Administrative, and Social Sciences, Alanya University, 07400, Alanya, Antalya, Turkiye. Electronic address:
Online communities such as Reddit offer neurodivergent individuals a unique space to express emotions, seek psychosocial support, and negotiate identity outside conventional social constraints. Understanding how these communities articulate and structure emotional discourse is essential for inclusive technology design. This study employed a hybrid natural language processing (NLP) framework that integrates lexicon-based sentiment analysis (VADER) with transformer-based topic modeling (BERTopic).
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