Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Screening and interpreting multi-item associations based on log-linear modeling
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 20th international conference on World wide web
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Predicting retweeting behavior based on autoregressive moving average model
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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Microblogging, as a new form of social media, attracts a huge number of users and becomes very popular. In this paper, we consider a fundamental social network issue that illustrates how information flows through a social media network and specify why users have different retweet behaviors. We propose to characterize social ties by using various features such as power ratio, local link structure, location, and gender. Those features can be directly extracted from users' profiles in Microblogging sites. We apply a fitted Log-linear model to describe association patterns among the features and retweet factor. Using the fitted Log-linear model, we explain why users with different profiles and link structures have different retweet behaviors. Our evaluations on Sina Weibo data set show several phenomenons.