Identification of intimate partner violence from free text descriptions in social media

Abstract

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, Naïve 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 typespecific 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/.

Systematic Investigation of Meta-Analysis Data on Treatment Effectiveness for Physical, Psychological, and Sexual Intimate Partner Violence Perpetration

“…Psychological violence can be defined as verbal and non-verbal communication perpetrated in order to mentally and emotionally harm the intimate partner (Breiding et al, 2015). Identifying psychological violence can be challenging since it involves abusive situations that need contextualization due to the broad range of behaviors that can be vague (Breiding et al, 2015;Trinh Ha et al, 2022). Explosive anger, coercive control, degradation, isolation, and manipulation are a few of the behaviors considered psychological violence (Follingstad, 2009;Jacobson & Gottman, 1998).…”

Characterizing the Differences in Descriptions of Violence on Reddit During the COVID-19 Pandemic

“…Then, we trained multiple machine learning classifiers, support vector machine, k -nearest neighbors (KNN), random forest (RF), decision tree, and naive Bayes for each type of violence. We chose these classifiers because they have previously been found to be effective in identifying IPV from social media data (Trinh Ha et al, 2022). For each type of violence, we applied 80% of the training data to train the classifiers and used the remaining 20% sampled data to calculate the accuracy of the classifiers.…”

Technological Innovations for Tackling Domestic Violence

“…Trinh Ha et al [27] used data sourced from questionnaires containing self-report data of intimate partner violence, and trained machine learning algorithms to detect abuse using text-based information. They trained three algorithms including SVM, Naive Bayes, Logistic Regression, and Random Forest.…”