{"title":"把这些点联系起来,推断Twitter上关注者的话题兴趣","authors":"Aastha Nigam, Salvador Aguiñaga, N. Chawla","doi":"10.1109/BESC.2016.7804498","DOIUrl":null,"url":null,"abstract":"Twitter provides a platform for information sharing and diffusion, and has quickly emerged as a mechanism for organizations to engage with their consumers. A driving factor for engagement is providing relevant and timely content to users. We posit that the engagement via tweets offers a good potential to discover user interests and leverage that information to target specific content of interest. To that end, we have developed a framework that analyzes tweets to identify the interests of current followers and leverages topic models to deliver a personalized topic profile for each user. We validated our framework by partnering up with a local media company and analyzing the content gap between them and their followers. We also developed a mobile application that incorporates the proposed framework.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Connecting the dots to infer followers' topical interest on Twitter\",\"authors\":\"Aastha Nigam, Salvador Aguiñaga, N. Chawla\",\"doi\":\"10.1109/BESC.2016.7804498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Twitter provides a platform for information sharing and diffusion, and has quickly emerged as a mechanism for organizations to engage with their consumers. A driving factor for engagement is providing relevant and timely content to users. We posit that the engagement via tweets offers a good potential to discover user interests and leverage that information to target specific content of interest. To that end, we have developed a framework that analyzes tweets to identify the interests of current followers and leverages topic models to deliver a personalized topic profile for each user. We validated our framework by partnering up with a local media company and analyzing the content gap between them and their followers. We also developed a mobile application that incorporates the proposed framework.\",\"PeriodicalId\":225942,\"journal\":{\"name\":\"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BESC.2016.7804498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BESC.2016.7804498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Connecting the dots to infer followers' topical interest on Twitter
Twitter provides a platform for information sharing and diffusion, and has quickly emerged as a mechanism for organizations to engage with their consumers. A driving factor for engagement is providing relevant and timely content to users. We posit that the engagement via tweets offers a good potential to discover user interests and leverage that information to target specific content of interest. To that end, we have developed a framework that analyzes tweets to identify the interests of current followers and leverages topic models to deliver a personalized topic profile for each user. We validated our framework by partnering up with a local media company and analyzing the content gap between them and their followers. We also developed a mobile application that incorporates the proposed framework.