{"title":"Topic Evolution Analysis in Social Networking Services: Taking Sina Weibo as an Example","authors":"Yuhui Wang","doi":"10.32604/csse.2018.33.287","DOIUrl":null,"url":null,"abstract":"Event-related topics in social networking services are always the epitome of heated society issues, therefore determining the significance of analyzing its evolution patterns. In this paper, we present a comprehensive survey on the tweets about \"ransomware\" in Sina Weibo, a famous social networking service similar to twitter in China. The keyword corresponds to a global ransomware attack in May 2017, on which our example event-related topics are based. We collect text data from sina Weibo and vectorize each tweets, before using a dynamic topic model to discover the event-related topics. The results of the topic model are explainable enough and help us to understand the evolution of those topics more thoroughly","PeriodicalId":119237,"journal":{"name":"Commun. Stat. Simul. Comput.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Commun. Stat. Simul. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32604/csse.2018.33.287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Event-related topics in social networking services are always the epitome of heated society issues, therefore determining the significance of analyzing its evolution patterns. In this paper, we present a comprehensive survey on the tweets about "ransomware" in Sina Weibo, a famous social networking service similar to twitter in China. The keyword corresponds to a global ransomware attack in May 2017, on which our example event-related topics are based. We collect text data from sina Weibo and vectorize each tweets, before using a dynamic topic model to discover the event-related topics. The results of the topic model are explainable enough and help us to understand the evolution of those topics more thoroughly