{"title":"自动内容分析中的分类错误会导致回归偏差。我们能解决这个问题吗？是的，我们能！","authors":"Nathan TeBlunthuis, Valerie Hase, Chung-Hong Chan","doi":"10.1080/19312458.2023.2293713","DOIUrl":null,"url":null,"abstract":"Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become ...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":"4 1","pages":""},"PeriodicalIF":6.3000,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!\",\"authors\":\"Nathan TeBlunthuis, Valerie Hase, Chung-Hong Chan\",\"doi\":\"10.1080/19312458.2023.2293713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become ...\",\"PeriodicalId\":47552,\"journal\":{\"name\":\"Communication Methods and Measures\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communication Methods and Measures\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1080/19312458.2023.2293713\",\"RegionNum\":1,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication Methods and Measures","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1080/19312458.2023.2293713","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!
Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become ...
期刊介绍：
Communication Methods and Measures aims to achieve several goals in the field of communication research. Firstly, it aims to bring attention to and showcase developments in both qualitative and quantitative research methodologies to communication scholars. This journal serves as a platform for researchers across the field to discuss and disseminate methodological tools and approaches.
Additionally, Communication Methods and Measures seeks to improve research design and analysis practices by offering suggestions for improvement. It aims to introduce new methods of measurement that are valuable to communication scientists or enhance existing methods. The journal encourages submissions that focus on methods for enhancing research design and theory testing, employing both quantitative and qualitative approaches.
Furthermore, the journal is open to articles devoted to exploring the epistemological aspects relevant to communication research methodologies. It welcomes well-written manuscripts that demonstrate the use of methods and articles that highlight the advantages of lesser-known or newer methods over those traditionally used in communication.
In summary, Communication Methods and Measures strives to advance the field of communication research by showcasing and discussing innovative methodologies, improving research practices, and introducing new measurement methods.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
