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Decoding Digital Discourse Through Multimodal Text and Image Machine Learning Models to Classify Sentiment and Detect Hate Speech in Race- and Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual Community–Related Posts on Social Media: Quantitative Study

Decoding Digital Discourse Through Multimodal Text and Image Machine Learning Models to Classify Sentiment and Detect Hate Speech in Race- and Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual Community–Related Posts on Social Media: Quantitative Study

Tweets referencing Middle Eastern and Black people had the highest proportion of negative sentiment. Furthermore, changes in negative racial sentiment were aligned with events salient to specific groups. For example, there were increases in negative racial sentiment tweets referencing Latinx people from 2015 to 2018, peaking at the end of 2018 with the midterm elections and national discussions of the border wall and immigration [13].

Thu T Nguyen, Xiaohe Yue, Heran Mane, Kyle Seelman, Penchala Sai Priya Mullaputi, Elizabeth Dennard, Amrutha S Alibilli, Junaid S Merchant, Shaniece Criss, Yulin Hswen, Quynh C Nguyen

J Med Internet Res 2025;27:e72822

The Prevalence and Incidence of Suicidal Thoughts and Behavior in a Smartphone-Delivered Treatment Trial for Body Dysmorphic Disorder: Cohort Study

The Prevalence and Incidence of Suicidal Thoughts and Behavior in a Smartphone-Delivered Treatment Trial for Body Dysmorphic Disorder: Cohort Study

Relevant clinician-administered measures included the Yale-Brown Obsessive Compulsive Scale Modified for BDD (BDD-YBOCS; [26]), Mini International Neuropsychiatric Interview (MINI 7.02; [27]), and the Columbia-Suicide Severity Rating Scale (C-SSRS [28]; refer to [12] for additional measures administered). The BDD-YBOCS is the gold-standard measure of BDD symptom severity and was used to characterize the sample and track symptom severity.

Adam C Jaroszewski, Natasha Bailen, Simay I Ipek, Jennifer L Greenberg, Susanne S Hoeppner, Hilary Weingarden, Ivar Snorrason, Sabine Wilhelm

JMIR Ment Health 2025;12:e63605

Characterizing Patient-Reported Fatigue Using Electronic Diaries in Neurodegenerative and Immune-Mediated Inflammatory Diseases: Observational Study

Characterizing Patient-Reported Fatigue Using Electronic Diaries in Neurodegenerative and Immune-Mediated Inflammatory Diseases: Observational Study

The distributions of the physical and mental fatigue questions from daily e Diary and FACIT-F overall scores are shown in Figure 4 A-C. Daily e Diary responses and FACIT-F scores are clustered around low fatigue levels. Distribution of responses. (A) Overall (total) FACIT-F score distribution, (B) physical fatigue from e Diary, and (C) mental fatigue from e Diary.

Adrien Bennetot, Rana Zia Ur Rehman, Robbin Romijnders, Zhi Li, Victoria Macrae, Kristen Davies, Wan-Fai Ng, Walter Maetzler, Jennifer Kudelka, Hanna Hildesheim, Kirsten Emmert, Emma Paulides, C Janneke van der Woude, Ralf Reilmann, Svenja Aufenberg, Meenakshi Chatterjee, Nikolay V Manyakov, Clémence Pinaud, Stefan Avey

JMIR Form Res 2025;9:e65879