Citation

Xue J, Chen J, Gelles R. Violence Gend. 2019; 6(2): 105-114.

Copyright

(Copyright © 2019, Mary Ann Liebert Publishers)

DOI

10.1089/vio.2017.0066

PMID

unavailable

Abstract

This study aims to discover hidden topics and thematic structures among domestic violence-related texts on Twitter. We collected 322,863 messages using the key term "domestic violence." We used unsupervised machine-learning methodology Latent dirichlet allocation, and found that the most common 20 pairs of words were "violence awareness," "greg hardy," "awareness month," "victims domestic," "stop domestic," and "ronda rousey." We identified 20 topics that appear most frequently, such as Topic 19 with frequent words "greg hardy," "photos greg," "dallas cowboys," "charges expunged," "hardy girlfriend," and also assigned themes (e.g., "Greg Hardy domestic violence case") for the topics. This study demonstrates the feasibility of using topic-modeling methods for mining gender-based violence data on Twitter.

Language: en