JOURNAL OF MEDICAL INTERNET RESEARCH Xue et al Original Paper The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets Jia Xue1,2, PhD; Junxiang Chen3, PhD; Chen Chen4, PhD; Ran Hu1, MA, MSW; Tingshao Zhu5, PhD 1Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada 2Faculty of Information, University of Toronto, Toronto, ON, Canada 3School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States 4Middleware System Research Group, University of Toronto, Toronto, ON, Canada 5Institute of Psychology, Chinese Academy of Sciences, Beijing, China Corresponding Author: Jia Xue, PhD Factor-Inwentash Faculty of Social Work University of Toronto 246 Bloor St W Toronto, ON, M5S 1V4 Canada Phone: 1 416 946 5429 Email:
[email protected] Abstract Background: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. Objective: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. Methods: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. Results: We extracted 9 themes from 1,015,874 tweets on family