{"title":"推特上新闻传播的流行模式","authors":"Saeed Abdullah, Xindong Wu","doi":"10.1109/ICTAI.2011.33","DOIUrl":null,"url":null,"abstract":"In this paper, we describe a novel approach to understand and explain news spreading dynamics on Twitter by using well-known epidemic models. Our underlying hypothesis is that the information diffusion on Twitter is analogous to the spread of a disease. As mathematical epidemiology has been extensively studied, being able to express news spreading as an epidemic model enables us to use a wide range of tools and procedures which have been proven to be both analytically rich and operationally useful. To further emphasize this point, we also show how we can readily use one of such tools -- a procedure for detection of influenza epidemics, to detect change of trend dynamics on Twitter.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"60","resultStr":"{\"title\":\"An Epidemic Model for News Spreading on Twitter\",\"authors\":\"Saeed Abdullah, Xindong Wu\",\"doi\":\"10.1109/ICTAI.2011.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe a novel approach to understand and explain news spreading dynamics on Twitter by using well-known epidemic models. Our underlying hypothesis is that the information diffusion on Twitter is analogous to the spread of a disease. As mathematical epidemiology has been extensively studied, being able to express news spreading as an epidemic model enables us to use a wide range of tools and procedures which have been proven to be both analytically rich and operationally useful. To further emphasize this point, we also show how we can readily use one of such tools -- a procedure for detection of influenza epidemics, to detect change of trend dynamics on Twitter.\",\"PeriodicalId\":332661,\"journal\":{\"name\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"60\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2011.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we describe a novel approach to understand and explain news spreading dynamics on Twitter by using well-known epidemic models. Our underlying hypothesis is that the information diffusion on Twitter is analogous to the spread of a disease. As mathematical epidemiology has been extensively studied, being able to express news spreading as an epidemic model enables us to use a wide range of tools and procedures which have been proven to be both analytically rich and operationally useful. To further emphasize this point, we also show how we can readily use one of such tools -- a procedure for detection of influenza epidemics, to detect change of trend dynamics on Twitter.
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