{"title":"从社交数据自动生成事件时间轴","authors":"Omar Alonso, S. Tremblay, Fernando Diaz","doi":"10.1145/3091478.3091519","DOIUrl":null,"url":null,"abstract":"Over the past few years, social media has seen phenomenal growth and has become a very important source for getting real time updates from different parts of the world. While the notion of a trend usually reflects current events, the amount of information accumulated over a period of time can be used to provide another perspective for such events in the form of a timeline. In this paper, we present a technique that uses social information as relevance surrogates to generate an informative timeline. A core component is a variation of pseudo relevance feedback that is automatically generated using social data without external evidence. Finally, we describe the implementation of such technique and present evaluation results using a real-world data set.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automatic Generation of Event Timelines from Social Data\",\"authors\":\"Omar Alonso, S. Tremblay, Fernando Diaz\",\"doi\":\"10.1145/3091478.3091519\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over the past few years, social media has seen phenomenal growth and has become a very important source for getting real time updates from different parts of the world. While the notion of a trend usually reflects current events, the amount of information accumulated over a period of time can be used to provide another perspective for such events in the form of a timeline. In this paper, we present a technique that uses social information as relevance surrogates to generate an informative timeline. A core component is a variation of pseudo relevance feedback that is automatically generated using social data without external evidence. Finally, we describe the implementation of such technique and present evaluation results using a real-world data set.\",\"PeriodicalId\":165747,\"journal\":{\"name\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3091478.3091519\",\"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 2017 ACM on Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3091478.3091519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Generation of Event Timelines from Social Data
Over the past few years, social media has seen phenomenal growth and has become a very important source for getting real time updates from different parts of the world. While the notion of a trend usually reflects current events, the amount of information accumulated over a period of time can be used to provide another perspective for such events in the form of a timeline. In this paper, we present a technique that uses social information as relevance surrogates to generate an informative timeline. A core component is a variation of pseudo relevance feedback that is automatically generated using social data without external evidence. Finally, we describe the implementation of such technique and present evaluation results using a real-world data set.