Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media

Aug 3, 2026

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Domestic violence against women in Iran remains a hidden social issue with chronic physical, mental, industrial, and economic consequences for women, children, and families, while also limiting access to mental health care for victims. Social media campaigns, however, generate large volumes of Persian-language text that can support analysis and early detection. This study analyzes and classifies Persian social-media text related to domestic violence and applies machine learning to predict the risk level. Tweets and captions collected from Twitter and Instagram (April 2020–April 2021) were labeled by expert criteria and evaluated through modeling.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Background and definition of domestic violence

Prevalence and barriers to disclosure

Relevance of social media and data availability

Study objective and approach

Data collection from Twitter and Instagram

Labeling criteria and dataset selection

Machine learning modeling and evaluation

Results

Model performance and key findings

Discussion and implications

University of Birmingham Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media Salehi, Meysam; Ghahari, Shahrbanoo; Hosseinzadeh, Mehdi; Ghalichi, Leila DOI: 10.1016/j.heliyon.2023.e15667 License: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): Salehi, M, Ghahari, S, Hosseinzadeh, M & Ghalichi, L 2023, 'Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media', Heliyon, vol. 9, no. 5, e15667 . [https://doi.org/10.1016/j.heliyon.2023.e15667](https://doi.org/10.1016/j.heliyon.2023.e15667) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 02. Aug. 2026 Heliyon 9 (2023) e15667 Contents lists available at ScienceDirect Heliyon journal [homepage: www.cell.com/heliyon](homepage: www.cell.com/heliyon) | Research article
Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media | | | | | --- | --- | --- | --- | | Meysam Salehia, Shahrbanoo Ghaharia, *, Mehdi Hosseinzadehb, Leila Ghalichi ba Department of Mental Health, School of Behavioral Sciences and Mental Health, Tehran Institute of Psychiatry, Iran University of Medical Sciences, Tehran, Iran
b Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Mental health Domestic violence
Machine learning Social media | | Domestic violence (DV) against women in Iran is a hidden societal issue. In addition to its chronic physical, mental, industrial, and economic effects on women, children, and families, DV prevents victims from receiving mental health care. On the other hand, DV campaigns on social media have encouraged victims and society to share their stories of abuse. As a result, massive amount of data has been generated about this violence, which can be used for analysis and early detection. Therefore, this study aimed to analyze and classify Persian textual content pertinent to DV against women in social media. It also aimed to use machine learning to predict the risk of this content. After collecting 53,105 tweets and captions in the Persian language from Twitter and Instagram, between April 2020 and April 2021, 1611 twe

FAQs

What problem does the study address?

The study addresses domestic violence against women in Iran as a hidden issue and leverages Persian social-media content to enable analysis and early detection.

How was the dataset collected and labeled?

The researchers collected 53,105 tweets and captions in Persian from Twitter and Instagram between April 2020 and April 2021, then randomly selected 1,611 items and categorized them using criteria compiled and approved by a domestic-violence expert.

Which machine learning model performed best?

The Naïve Base model achieved the highest accuracy (86.77%) among the evaluated machine learning models for predicting critical Persian content related to domestic violence.

Aug 3, 2026

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