Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Finding influentials based on the temporal order of information adoption in twitter
Proceedings of the 19th international conference on World wide web
International Journal of Human-Computer Studies
Cross-media impact on twitter in japan
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Social media analytics: tracking, modeling and predicting the flow of information through networks
Proceedings of the 20th international conference companion on World wide web
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Identifying breakpoints in public opinion
Proceedings of the First Workshop on Social Media Analytics
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
Information transfer in social media
Proceedings of the 21st international conference on World Wide Web
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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In recent years, online social networks become popular across the world. The success of online social networks, such as Twitter and Facebook, have led to the emergence of similar social networks in many countries with different cultural backgrounds. For instance, Sina Weibo and Tencent Weibo are the most popular microblog services in China. One interesting question is what are the similarities and differences between these social networks. In particular, we are interested in finding out whether similar types of information propagates similarly or not on a similar kind of social network service. In this paper, we analyze two representative microblog service providers, Twitter from U.S. and Tencent Weibo from China. We focus on the patterns of event-driven information propagation. We employ several metrics to measure the differences in information propagation patterns on a variety of selected topic categories. Surprisingly, the preliminary results of our study show that there is no significant difference between the two platforms in terms of information propagation patterns. This opens up further investigations to understand the factors as work.