Intimate Partner/Sexual Violence, Twitter Data, and Machine Learning Approach
About

Intimate partner violence and sexual violence are the most common forms of violence against women, affecting as many as one-third of women worldwide. We know little about the nature and content of IPV-related posts on Twitter. Thus, the goal of this project is to identify IPV/sexual violence-related contents within Twitter’s conversational data. The result of our exploratory research may have implications for IPV scholars and practitioners by opening up a new source of data and information about IPV. The study provides a unique view of IPV information on Twitter by linking social science with advanced statistics methods to better understand violence against women in the current social media environment.

Publications
Using Data Mining Techniques to Examine Domestic Violence Topics on Twitter
Xue, J., Chen, J., & Gelles, R. J. (2019).
Violence and Gender. 6 (2). 105-114
Harnessing Big Data for Social Justice: An Exploration of Violence against Women-Related Conversations on Twitter
Xue, J., Macropol, K., Jia. Y., Zhu, T., & Gelles, R. J. (2019).
Human Behavior and Emerging Technologies. 1(3). 269-279.
The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
Xue, J., Chen, J., Chen, C., Hu, R. & Zhu, T. (2020).
Journal OF Medical Internet Research, 22(11):e24361 doi: 10.2196/24361
Affiliated Faculties

Professor

Social Policy & Practice

University of Pennsylvania

Associate Professor

Computer Science and Mathematics

Arcadia University

Associate Professor

Computer Science and Mathematics

Arcadia University