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Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in 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 encoura...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172903/ https://www.ncbi.nlm.nih.gov/pubmed/37180917 http://dx.doi.org/10.1016/j.heliyon.2023.e15667 |
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author | Salehi, Meysam Ghahari, Shahrbanoo Hosseinzadeh, Mehdi Ghalichi, Leila |
author_facet | Salehi, Meysam Ghahari, Shahrbanoo Hosseinzadeh, Mehdi Ghalichi, Leila |
author_sort | Salehi, Meysam |
collection | PubMed |
description | 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 tweets and captions were chosen at random and categorized using criteria compiled and approved by an expert in the field of DV. Then, using machine learning algorithms, modeling and evaluation processes were performed on the tagged data. The Naïve Base model, with an accuracy of 86.77% was the most accurate model among all machine learning models for predicting critical Persian content pertinent to domestic violence on social media. The obtained findings indicate that using a machine learning approach, the risk of Persian content related to DV in social media against women can be predicted. |
format | Online Article Text |
id | pubmed-10172903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-101729032023-05-12 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 Heliyon Research Article 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 tweets and captions were chosen at random and categorized using criteria compiled and approved by an expert in the field of DV. Then, using machine learning algorithms, modeling and evaluation processes were performed on the tagged data. The Naïve Base model, with an accuracy of 86.77% was the most accurate model among all machine learning models for predicting critical Persian content pertinent to domestic violence on social media. The obtained findings indicate that using a machine learning approach, the risk of Persian content related to DV in social media against women can be predicted. Elsevier 2023-04-23 /pmc/articles/PMC10172903/ /pubmed/37180917 http://dx.doi.org/10.1016/j.heliyon.2023.e15667 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Salehi, Meysam Ghahari, Shahrbanoo Hosseinzadeh, Mehdi Ghalichi, Leila Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title | Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title_full | Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title_fullStr | Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title_full_unstemmed | Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title_short | Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media |
title_sort | domestic violence risk prediction in iran using a machine learning approach by analyzing persian textual content in social media |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172903/ https://www.ncbi.nlm.nih.gov/pubmed/37180917 http://dx.doi.org/10.1016/j.heliyon.2023.e15667 |
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