{"title":"谁的数据痕迹,谁的声音?在线参与的不平等及其对推荐系统研究的重要性","authors":"E. Hargittai","doi":"10.1145/3298689.3347066","DOIUrl":null,"url":null,"abstract":"As research relies on data traces about people's online behavior, it is important to take a step back and ask: who uses the systems where these traces appear? This talk will discuss online participation from a digital-inequality perspective showing how differences in online behavior vary by socio-demographic characteristics as well as people's Internet skills. The presentation breaks down the various steps necessary for engagement - the pipeline of online participation - and shows that different factors explain different parts of the pipeline with skills mattering at all stages. Drawing on several data sets, the talk explores whose traces are most likely to show up on various systems and what this means for potential biases in what researchers draw from analyzing digital trace data.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"271 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems research\",\"authors\":\"E. Hargittai\",\"doi\":\"10.1145/3298689.3347066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As research relies on data traces about people's online behavior, it is important to take a step back and ask: who uses the systems where these traces appear? This talk will discuss online participation from a digital-inequality perspective showing how differences in online behavior vary by socio-demographic characteristics as well as people's Internet skills. The presentation breaks down the various steps necessary for engagement - the pipeline of online participation - and shows that different factors explain different parts of the pipeline with skills mattering at all stages. Drawing on several data sets, the talk explores whose traces are most likely to show up on various systems and what this means for potential biases in what researchers draw from analyzing digital trace data.\",\"PeriodicalId\":215384,\"journal\":{\"name\":\"Proceedings of the 13th ACM Conference on Recommender Systems\",\"volume\":\"271 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3298689.3347066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3298689.3347066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems research
As research relies on data traces about people's online behavior, it is important to take a step back and ask: who uses the systems where these traces appear? This talk will discuss online participation from a digital-inequality perspective showing how differences in online behavior vary by socio-demographic characteristics as well as people's Internet skills. The presentation breaks down the various steps necessary for engagement - the pipeline of online participation - and shows that different factors explain different parts of the pipeline with skills mattering at all stages. Drawing on several data sets, the talk explores whose traces are most likely to show up on various systems and what this means for potential biases in what researchers draw from analyzing digital trace data.