{"title":"Mining social media: issues and challenges","authors":"Huan Liu","doi":"10.1145/2072609.2072611","DOIUrl":"https://doi.org/10.1145/2072609.2072611","url":null,"abstract":"The prevalence of social media offers a new kind of laboratory for behavioral study. In the era of the social Web, we are presented with unparalleled opportunities and novel challenges. In this talk, we will use some of our recent studies of human behavior to illustrate our endeavors to improve the understanding of human behaviors in social media. We explore how one can efficiently gauge what's happening in social media with an inordinate number of groups and growing; inquire whether one can disentangle the complicated connections among users to find their group memberships; look into user migration patterns in the presence of seemingly unlimited choices of social media services; and investigate ways of exploiting vulnerability to protect user privacy on a social networking site. We can benefit significantly from extant sociological theories and methodologies in carrying out interdisciplinary research that sheds lights in mining social media. The improved understanding of human behavior can help develop social media services that encourage more user participation with better experience in growing social media activities.","PeriodicalId":255184,"journal":{"name":"Proceedings of the 3rd ACM SIGMM international workshop on Social media - WSM '11","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134355203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using social media to identify events","authors":"Xueliang Liu, Raphael Troncy, B. Huet","doi":"10.1145/2072609.2072613","DOIUrl":"https://doi.org/10.1145/2072609.2072613","url":null,"abstract":"We present a method to automatically detect and identify events from social media sharing web sites. Our approach is based on the observation that many photos and videos are taken and shared when events occur. We select 9 venues across the globe that demonstrate a significant activity according to the EventMedia dataset and we thoroughly evaluate our approach against an official ground truth obtained directly by scraping the event venues' web sites. The results show our ability to not only detect events with high accuracy but also mine and identify events that have not been published in popular event directories such as Last.fm, Eventful or Upcoming. In addition to the textual identification of events, we show how we can build visual summaries of past events providing viewers with a more compelling feeling of the event's atmosphere.","PeriodicalId":255184,"journal":{"name":"Proceedings of the 3rd ACM SIGMM international workshop on Social media - WSM '11","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}