@article{ref1,
title="Using data mining techniques to examine domestic violence topics on Twitter",
journal="Violence and gender",
year="2019",
author="Xue, Jia and Chen, Junxiang and Gelles, Richard",
volume="6",
number="2",
pages="105-114",
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 &quot;domestic violence.&quot; We used unsupervised machine-learning methodology Latent dirichlet allocation, and found that the most common 20 pairs of words were &quot;violence awareness,&quot; &quot;greg hardy,&quot; &quot;awareness month,&quot; &quot;victims domestic,&quot; &quot;stop domestic,&quot; and &quot;ronda rousey.&quot; We identified 20 topics that appear most frequently, such as Topic 19 with frequent words &quot;greg hardy,&quot; &quot;photos greg,&quot; &quot;dallas cowboys,&quot; &quot;charges expunged,&quot; &quot;hardy girlfriend,&quot; and also assigned themes (e.g., &quot;Greg Hardy domestic violence case&quot;) for the topics. This study demonstrates the feasibility of using topic-modeling methods for mining gender-based violence data on Twitter.<p /> <p>Language: en</p>",
language="en",
issn="2326-7836",
doi="10.1089/vio.2017.0066",
url="http://dx.doi.org/10.1089/vio.2017.0066"
}