DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390135
Yusheng Xie, Yu Cheng, Daniel Honbo, Kunpeng Zhang, Ankit Agrawal, A. Choudhary, Yi Gao, Jiangtao Gou
{"title":"Probabilistic macro behavioral targeting","authors":"Yusheng Xie, Yu Cheng, Daniel Honbo, Kunpeng Zhang, Ankit Agrawal, A. Choudhary, Yi Gao, Jiangtao Gou","doi":"10.1145/2390131.2390135","DOIUrl":"https://doi.org/10.1145/2390131.2390135","url":null,"abstract":"We investigate a class of emerging online marketing challenges in social networks; and formally, we define macro behavioral targeting (MBT) to be the marketing efforts that appeal to a massive targeted population with non-personalized broadcasting. Upon the problem formulation, we describe a probabilistic graphical model for MBT. In our model, we derive the prior distributions from scratch because existing applications of graphical model / Bayesian network cannot fully capture the unique characteristics of MBT. In the derivation, we propose an approximation method to circumvent an intractable situation where order statistics need be calculated from exponentially increasing computations. In the experiments, we present case studies on real Facebook data.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117001379","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}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390142
Aditi Gupta, A. Joshi, P. Kumaraguru
{"title":"Identifying and characterizing user communities on Twitter during crisis events","authors":"Aditi Gupta, A. Joshi, P. Kumaraguru","doi":"10.1145/2390131.2390142","DOIUrl":"https://doi.org/10.1145/2390131.2390142","url":null,"abstract":"Twitter is a prominent online social media which is used to share information and opinions. Previous research has shown that current real world news topics and events dominate the discussions on Twitter. In this paper, we present a preliminary study to identify and characterize communities from a set of users who post messages on Twitter during crisis events. We present our work in progress by analyzing three major crisis events of 2011 as case studies (Hurricane Irene, Riots in England, and Earthquake in Virginia). Hurricane Irene alone, caused a damage of about 7-10 billion USD and claimed 56 lives. The aim of this paper is to identify the different user communities, and characterize them by the top central users. First, we defined a similarity metric between users based on their links, content posted and meta-data. Second, we applied spectral clustering to obtain communities of users formed during three different crisis events. Third, we evaluated the mechanism to identify top central users using degree centrality; we showed that the top users represent the topics and opinions of all the users in the community with 81% accuracy on an average. The top central people identified represent what the entire community shares. Therefore to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565649","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}