Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support.

Yuting Guo, Sangmi Kim, Elise Warren, Yuan-Chi Yang, Sahithi Lakamana, Abeed Sarker
{"title":"Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support.","authors":"Yuting Guo, Sangmi Kim, Elise Warren, Yuan-Chi Yang, Sahithi Lakamana, Abeed Sarker","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Social media platforms are increasingly being used by intimate partner violence (IPV) victims to share experiences and seek support. If such information is automatically curated, it may be possible to conduct social media based surveillance and even design interventions over such platforms. In this paper, we describe the development of a supervised classification system that automatically characterizes IPV-related posts on the social network Reddit. We collected data from four IPV-related subreddits and manually annotated the data to indicate whether a post is a self-report of IPV or not. Using the annotated data (N=289), we trained, evaluated, and compared supervised machine learning systems. A transformer-based classifier, RoBERTa, obtained the best classification performance with overall accuracy of 78% and IPV-self-report class 𝐹<sub>1</sub> -score of 0.67. Post-classification error analyses revealed that misclassifications often occur for posts that are very long or are non-first-person reports of IPV. Despite the relatively small annotated data, our classification methods obtained promising results, indicating that it may be possible to detect and, hence, provide support to IPV victims over Reddit.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283132/pdf/2018.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Social media platforms are increasingly being used by intimate partner violence (IPV) victims to share experiences and seek support. If such information is automatically curated, it may be possible to conduct social media based surveillance and even design interventions over such platforms. In this paper, we describe the development of a supervised classification system that automatically characterizes IPV-related posts on the social network Reddit. We collected data from four IPV-related subreddits and manually annotated the data to indicate whether a post is a self-report of IPV or not. Using the annotated data (N=289), we trained, evaluated, and compared supervised machine learning systems. A transformer-based classifier, RoBERTa, obtained the best classification performance with overall accuracy of 78% and IPV-self-report class 𝐹1 -score of 0.67. Post-classification error analyses revealed that misclassifications often occur for posts that are very long or are non-first-person reports of IPV. Despite the relatively small annotated data, our classification methods obtained promising results, indicating that it may be possible to detect and, hence, provide support to IPV victims over Reddit.

从社交媒体自动检测亲密伴侣暴力受害者,以主动提供支持。
亲密伴侣暴力 (IPV) 受害者越来越多地使用社交媒体平台来分享经历和寻求支持。如果能对此类信息进行自动整理,就有可能在此类平台上进行基于社交媒体的监控,甚至设计干预措施。在本文中,我们介绍了一个监督分类系统的开发过程,该系统可自动描述社交网络 Reddit 上与 IPV 相关的帖子。我们从四个与 IPV 相关的 subreddits 中收集了数据,并对数据进行了人工标注,以表明帖子是否是 IPV 的自我报告。利用注释数据(N=289),我们对监督机器学习系统进行了训练、评估和比较。基于转换器的分类器 RoBERTa 获得了最好的分类效果,总体准确率为 78%,IPV 自我报告类的ᵃ1 分数为 0.67。分类后误差分析表明,对于篇幅很长或非第一人称的 IPV 报告,经常会出现分类错误。尽管注释数据相对较少,但我们的分类方法仍取得了可喜的成果,这表明我们有可能在 Reddit 上检测到 IPV 受害者并为其提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信