Bi Li, Boyu Chen, Yan Wu, Juan Wang, Xueming Yan, Yahui Yang
{"title":"从数字痕迹看微博使用动机","authors":"Bi Li, Boyu Chen, Yan Wu, Juan Wang, Xueming Yan, Yahui Yang","doi":"10.1145/3443279.3443294","DOIUrl":null,"url":null,"abstract":"Billions of users around the world are using social networking sites (SNS) to express everyday thoughts and feelings. Investigating motives of using SNS is attracting scholarly attention. The common way to assess users' motives is analyzing data from self-report questionnaires. The current research aims to identifying undergraduate students' motives of using Weibo from digital traces, in an effort to alleviate the distortion in self-report data. The term frequency-inverse document frequency (Tf-idf) was employed to obtain key terms and their weights in digital traces crawled from Weibo. Top frequent terms, based on Tf-idf, indicate that entertainment, information seeking and sharing, and alleviating life stress are among the major motives of using Weibo. This study underscores the feasibility and importance of directly detecting motives of using SNS from digital traces.","PeriodicalId":414366,"journal":{"name":"Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying the Motives of Using Weibo from Digital Traces\",\"authors\":\"Bi Li, Boyu Chen, Yan Wu, Juan Wang, Xueming Yan, Yahui Yang\",\"doi\":\"10.1145/3443279.3443294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Billions of users around the world are using social networking sites (SNS) to express everyday thoughts and feelings. Investigating motives of using SNS is attracting scholarly attention. The common way to assess users' motives is analyzing data from self-report questionnaires. The current research aims to identifying undergraduate students' motives of using Weibo from digital traces, in an effort to alleviate the distortion in self-report data. The term frequency-inverse document frequency (Tf-idf) was employed to obtain key terms and their weights in digital traces crawled from Weibo. Top frequent terms, based on Tf-idf, indicate that entertainment, information seeking and sharing, and alleviating life stress are among the major motives of using Weibo. This study underscores the feasibility and importance of directly detecting motives of using SNS from digital traces.\",\"PeriodicalId\":414366,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3443279.3443294\",\"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 4th International Conference on Natural Language Processing and Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3443279.3443294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying the Motives of Using Weibo from Digital Traces
Billions of users around the world are using social networking sites (SNS) to express everyday thoughts and feelings. Investigating motives of using SNS is attracting scholarly attention. The common way to assess users' motives is analyzing data from self-report questionnaires. The current research aims to identifying undergraduate students' motives of using Weibo from digital traces, in an effort to alleviate the distortion in self-report data. The term frequency-inverse document frequency (Tf-idf) was employed to obtain key terms and their weights in digital traces crawled from Weibo. Top frequent terms, based on Tf-idf, indicate that entertainment, information seeking and sharing, and alleviating life stress are among the major motives of using Weibo. This study underscores the feasibility and importance of directly detecting motives of using SNS from digital traces.