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

Concerns have been raised over the experiences of violence such as domestic violence (DV) and intimate partner violence (IPV) during the COVID-19 pandemic. Social media such as Reddit represent an alternative outlet for reporting experiences of violence where healthcare access has been limited. This study analyzed seven violence-related subreddits to investigate the trends of different violence patterns from January 2018 to February 2022 to enhance the health-service providers' existing service or provide some new perspective for existing violence research. Specifically, we collected violence-related texts from Reddit using keyword searching and identified six major types with supervised machine learning classifiers: DV, IPV, physical violence, sexual violence, emotional violence, and nonspecific violence or others. The increase rate (IR) of each violence type was calculated and temporally compared in five phases of the pandemic. The phases include one pre-pandemic phase (Phase 0, the date before February 26, 2020) and four pandemic phases (Phases 1-4) with separation dates of June 17, 2020, September 7, 2020, and June 4, 2021. We found that the number of IPV-related posts increased most in the earliest phase; however, that for COVID-citing IPV was highest in the mid-pandemic phase. IRs for DV, IPV, and emotional violence also showed increases across all pandemic phases, with IRs of 26.9%, 58.8%, and 28.8%, respectively, from the pre-pandemic to the first pandemic phase. In the other three pandemic phases, all the IRs for these three types of violence were positive, though lower than the IRs in the first pandemic phase. The findings highlight the importance of identifying and providing help to those who suffer from such violent experiences and support the role of social media site monitoring as a means of informative surveillance for help-providing authorities and violence research groups.

A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research.

Purpose: Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work. Methods: This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review. Results: The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research. Conclusions: Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches.