Simha Mummalaneni, Rebecca Jen-Hui Wang, Mathew S. Isaac
{"title":"适合候选人的电子邮件营销活动:利用自动文本分析增加政治捐款","authors":"Simha Mummalaneni, Rebecca Jen-Hui Wang, Mathew S. Isaac","doi":"10.1177/10949968241240453","DOIUrl":null,"url":null,"abstract":"This research employs automated text analysis to explore how textual characteristics in campaign emails affect monetary donations received by political candidates. The authors outline a new methodological framework that combines a machine learning approach for natural language processing with fixed effect regressions, thereby enabling researchers to study and interpret the impact of textual characteristics on donations while also accounting for individual differences across candidates and their email recipients. Using this framework, the authors analyze 764 emails from 19 candidates in the 2020 U.S. Democratic presidential primary election and evaluate how certain textual characteristics (e.g., empathy, vulnerability) in campaign emails affect donation outcomes. Identifying these effects would enable candidates to improve their email text and increase their donations by 9% on average. This research provides a practical and flexible roadmap for automated text analysis in situations where political campaigns do not have clear a priori hypotheses about which textual characteristics will be effective for them.","PeriodicalId":6,"journal":{"name":"ACS Applied Nano Materials","volume":"69 3","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Email Campaigns That Suit the Candidate: Leveraging Automated Text Analysis to Increase Political Donations\",\"authors\":\"Simha Mummalaneni, Rebecca Jen-Hui Wang, Mathew S. Isaac\",\"doi\":\"10.1177/10949968241240453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research employs automated text analysis to explore how textual characteristics in campaign emails affect monetary donations received by political candidates. The authors outline a new methodological framework that combines a machine learning approach for natural language processing with fixed effect regressions, thereby enabling researchers to study and interpret the impact of textual characteristics on donations while also accounting for individual differences across candidates and their email recipients. Using this framework, the authors analyze 764 emails from 19 candidates in the 2020 U.S. Democratic presidential primary election and evaluate how certain textual characteristics (e.g., empathy, vulnerability) in campaign emails affect donation outcomes. Identifying these effects would enable candidates to improve their email text and increase their donations by 9% on average. This research provides a practical and flexible roadmap for automated text analysis in situations where political campaigns do not have clear a priori hypotheses about which textual characteristics will be effective for them.\",\"PeriodicalId\":6,\"journal\":{\"name\":\"ACS Applied Nano Materials\",\"volume\":\"69 3\",\"pages\":\"\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Nano Materials\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1177/10949968241240453\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Nano Materials","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10949968241240453","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Email Campaigns That Suit the Candidate: Leveraging Automated Text Analysis to Increase Political Donations
This research employs automated text analysis to explore how textual characteristics in campaign emails affect monetary donations received by political candidates. The authors outline a new methodological framework that combines a machine learning approach for natural language processing with fixed effect regressions, thereby enabling researchers to study and interpret the impact of textual characteristics on donations while also accounting for individual differences across candidates and their email recipients. Using this framework, the authors analyze 764 emails from 19 candidates in the 2020 U.S. Democratic presidential primary election and evaluate how certain textual characteristics (e.g., empathy, vulnerability) in campaign emails affect donation outcomes. Identifying these effects would enable candidates to improve their email text and increase their donations by 9% on average. This research provides a practical and flexible roadmap for automated text analysis in situations where political campaigns do not have clear a priori hypotheses about which textual characteristics will be effective for them.
期刊介绍:
ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.