{"title":"对于取证和安全环境中的大型在线数据集的分析,LIWC是否可靠、高效和有效?","authors":"Madison Hunter, 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":2.1000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Is LIWC reliable, efficient, and effective for the analysis of large online datasets in forensic and security contexts?\",\"authors\":\"Madison Hunter, 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\":2.1000,\"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}","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
摘要
本文评估了语言查询和单词计数(LIWC)的可靠性、效率和有效性。Boyd et al., 2022)对白人民族主义论坛的分析。这一点很重要,因为LIWC一直是许多研究和许多在法医或安全环境中检查极端主义内容的首选计算工具。因此,我们的目的是了解LIWC是否可以用于大规模分析;在这里,我们首先使用来自仅8个用户的帖子的小样本来检查这一点,并手动检查程序对类别子集的自动编码。我们的结果表明,LIWC编码不可靠,某些类别的准确率低至49.6%,召回率低至41.7%。对这些结果进行大量的人工校正是可能的,但这破坏了它对大型数据集的据称效率。
Is LIWC reliable, efficient, and effective for the analysis of large online datasets in forensic and security contexts?
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.