Intimate Partner Violence (IPV) is a significant public health problem that adversely
affects the well-being of victims. IPV is often under-reported and non-physical forms of
violence may not be recognized as IPV, even by victims. With the increasing popularity
of social media and due to the anonymity provided by some of these platforms, people
feel comfortable sharing descriptions of their relationship problems in social media.
The content generated in these platforms can be useful in identifying IPV and
characterizing the prevalence, causes, consequences, and correlates of IPV in broad
populations. However, these descriptions are in the form of free text and no corpus of
labeled data is available to perform large-scale computational and statistical analyses.
Here, we use data from established questionnaires that are used to collect self-report data on IPV to train machine learning models to predict IPV from free text. Using
Universal Sentence Encoder (USE) along with multiple machine learning algorithms
(Random Forest, SVM, Logistic Regression, Naive Bayes), we develop DETECTIPV,
a tool for detecting IPV in free text. Using DETECTIPV, we comprehensively
characterize the predictability of different types of violence (Physical Abuse, Emotional
Abuse, Sexual Abuse) from free text. Our results show that a general model that is
trained using examples of all violence types can identify IPV from free text with area
under the ROC curve (AUROC) 89%. We also train type-specific models and observe
that Physical Abuse can be identified with greatest accuracy (AUROC 98%), while
Sexual Abuse can be identified with high precision but relatively low recall. While our
results indicate that the prediction of Emotional Abuse is the most challenging,
DETECTIPV can identify Emotional Abuse with AUROC above 80%. These results
establish DETECTIPV as a tool that can be used to reliably detect IPV in the context of
various applications, ranging from flagging social media posts to detecting IPV in large
text corpuses for research purposes. DETECTIPV is available as a web service at
https://ipvlab.case.edu/ipvdetect/.