Comparing the pulses of categorical hot events in Twitter and Weibo
|Comparing the pulses of categorical hot events in Twitter and Weibo|
|Author(s)||Shuai X., Liu X., Xia T., Wu Y., Guo C.|
|Published in||HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media|
|Keyword(s)||click log mining, community comparison, information diffusion, information retrieval, social media, twitter, weibo, wikipedia (Extra: Hypertext systems, Information retrieval, World Wide Web, community comparison, Information diffusion, Log mining, Social media, twitter, weibo, Wikipedia, Social networking (online))|
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Comparing the pulses of categorical hot events in Twitter and Weibo is a 2014 conference paper written in English by Shuai X., Liu X., Xia T., Wu Y., Guo C. and published in HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media.
The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While traditional studies aim to learn the similarities and differences between these communities via high-cost user studies, in this paper we propose a much more efficient method to compare different communities by utilizing social media. Specifically, Weibo and Twitter, the two largest microblogging systems, are employed to represent the target communities, i.e. China and the Western world (mainly United States), respectively. Meanwhile, through the analysis of the Wikipedia page-click log, we identify a set of categorical 'hot events' for one month in 2012 and search those hot events in Weibo and Twitter corpora along with timestamps via information retrieval methods. We further quantitatively and qualitatively compare users' responses to those events in Twitter and Weibo in terms of three aspects: popularity, temporal dynamic, and information diffusion. The comparative results show that although the popularity ranking of those events are very similar, the patterns of temporal dynamics and information diffusion can be quite different.
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