{"title":"特朗普越多越好?","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.14","DOIUrl":null,"url":null,"abstract":"This chapter looks at an argument between two researchers concerning the epistemology, methodology, and ethics of data science versus traditional science in studying fertility. One of the researchers questions the other's use of Twitter data to examine fertility. The other researcher's defence is that Twitter data can be used to calculate a proxy fertility rate, comparing rates of women with and without children, looking at family changes, mapping geographic patterns of the tweets, but they were only partially using the data for this. They were mainly interested in soft measures concerning fertility, such as attitudes, values, feelings, and intentions. And about related issues such as family planning, abortion, and overpopulation. In particular, they can get a sense of sentiment: whether people are positive or negative about parenthood, whether they are tired, overjoyed, or depressed. However, the first researcher was not convinced because their approach to understanding fertility starts from a very different place — one driven by theory and hypotheses.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"So More Trumps Better?\",\"authors\":\"Rob Kitchin\",\"doi\":\"10.2307/j.ctv1c9hmnq.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter looks at an argument between two researchers concerning the epistemology, methodology, and ethics of data science versus traditional science in studying fertility. One of the researchers questions the other's use of Twitter data to examine fertility. The other researcher's defence is that Twitter data can be used to calculate a proxy fertility rate, comparing rates of women with and without children, looking at family changes, mapping geographic patterns of the tweets, but they were only partially using the data for this. They were mainly interested in soft measures concerning fertility, such as attitudes, values, feelings, and intentions. And about related issues such as family planning, abortion, and overpopulation. In particular, they can get a sense of sentiment: whether people are positive or negative about parenthood, whether they are tired, overjoyed, or depressed. However, the first researcher was not convinced because their approach to understanding fertility starts from a very different place — one driven by theory and hypotheses.\",\"PeriodicalId\":446623,\"journal\":{\"name\":\"Data Lives\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Lives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2307/j.ctv1c9hmnq.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Lives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2307/j.ctv1c9hmnq.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This chapter looks at an argument between two researchers concerning the epistemology, methodology, and ethics of data science versus traditional science in studying fertility. One of the researchers questions the other's use of Twitter data to examine fertility. The other researcher's defence is that Twitter data can be used to calculate a proxy fertility rate, comparing rates of women with and without children, looking at family changes, mapping geographic patterns of the tweets, but they were only partially using the data for this. They were mainly interested in soft measures concerning fertility, such as attitudes, values, feelings, and intentions. And about related issues such as family planning, abortion, and overpopulation. In particular, they can get a sense of sentiment: whether people are positive or negative about parenthood, whether they are tired, overjoyed, or depressed. However, the first researcher was not convinced because their approach to understanding fertility starts from a very different place — one driven by theory and hypotheses.