Is LIWC reliable, efficient, and effective for the analysis of large online datasets in forensic and security contexts?

Madison Hunter, Tim Grant
{"title":"Is LIWC reliable, efficient, and effective for the analysis of large online datasets in forensic and security contexts?","authors":"Madison Hunter,&nbsp;Tim Grant","doi":"10.1016/j.acorp.2025.100118","DOIUrl":null,"url":null,"abstract":"<div><div>This article evaluates the reliability, efficiency, and effectiveness of Linguistic Inquiry and Word Count (LIWC; Boyd et al., 2022) for the analysis of a white nationalist forum. This is important because LIWC has been the computational tool of choice for scores of studies generally and many examining extremist content in a forensic or security context. Our purpose, therefore, is to understand whether LIWC can be depended upon for large-scale analyses; we initially examine this here using a small sample of posts from a set of just eight users and manually checking the program's automated codings of a subset of categories. Our results show that the LIWC coding cannot be relied upon – precision falls to as low as 49.6 % and recall as low as 41.7 % for some categories. It would be possible to engage in considerable manual correction of these results, but this undermines its purported efficiency for large datasets.</div></div>","PeriodicalId":72254,"journal":{"name":"Applied Corpus Linguistics","volume":"5 1","pages":"Article 100118"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Corpus Linguistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666799125000012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This article evaluates the reliability, efficiency, and effectiveness of Linguistic Inquiry and Word Count (LIWC; Boyd et al., 2022) for the analysis of a white nationalist forum. This is important because LIWC has been the computational tool of choice for scores of studies generally and many examining extremist content in a forensic or security context. Our purpose, therefore, is to understand whether LIWC can be depended upon for large-scale analyses; we initially examine this here using a small sample of posts from a set of just eight users and manually checking the program's automated codings of a subset of categories. Our results show that the LIWC coding cannot be relied upon – precision falls to as low as 49.6 % and recall as low as 41.7 % for some categories. It would be possible to engage in considerable manual correction of these results, but this undermines its purported efficiency for large datasets.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Corpus Linguistics
Applied Corpus Linguistics Linguistics and Language
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
70 days
×
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