What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A Study on Detecting Patterns in Twitter Intra-topic User and Message Clustering
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Measuring message propagation and social influence on Twitter.com
SocInfo'10 Proceedings of the Second international conference on Social informatics
ALPOS: A Machine Learning Approach for Analyzing Microblogging Data
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Truthy: mapping the spread of astroturf in microblog streams
Proceedings of the 20th international conference companion on World wide web
Proceedings of the 20th international conference on World wide web
Learning Sentimental Influence in Twitter
ICFCSA '11 Proceedings of the 2011 International Conference on Future Computer Sciences and Application
On word-of-mouth based discovery of the web
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
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The explosive growth of microblogs has attracted many corporations and organizations. Microblogging has been considered as a high-quality advertising platform. In this study, we attempt to reveal the patterns of advertisement propagation in Sina-Microblog through analyzing a selected set of message cascades. Each message cascade is represented by a propagation tree and 33 features were extracted, which cover mainly three aspects of a cascade: the volume of the participants, the topology of the propagation paths, and the promptness of the propagation in term of time. To reveal the propagation patterns, We then group these message cascades using K-means clustering algorithm. Analysis of the resulted clusters reveals the patterns of advertisement propagation, based on which we further propose several metrics to measure the effectiveness of advertisement in microblogs.