Sina Weibo, which was launched in 2009, is the most popular Chinese micro-blogging service. It has been reported that Sina Weibo has more than 400 million registered users by the end of the third quarter in 2012. Sina Weibo and Twitter have a lot in common, however, in terms of the following preference, Sina Weibo users, most of whom are Chinese, behave differently compared with those of Twitter. This work is based on a data set of Sina Weibo which contains 80.8 million users' profiles and 7.2 billion relations and a large data set of Twitter. Firstly some basic features of Sina Weibo and Twitter are analyzed such as degree and activeness distribution, correlation between degree and activeness, and the degree of separation. Then the following preference is investigated by studying the assortative mixing, friend similarities, following distribution, edge balance ratio, and ranking correlation, where edge balance ratio is newly proposed to measure balance property of graphs. It is found that Sina Weibo has a lower reciprocity rate, more positive balanced relations and is more disassortative. Coinciding with Asian traditional culture, the following preference of Sina Weibo users is more concentrated and hierarchical: they are more likely to follow people at higher or the same social levels and less likely to follow people lower than themselves. In contrast, the same kind of following preference is weaker in Twitter. Twitter users are open as they follow people from levels, which accords with its global characteristic and the prevalence of western civilization. The message forwarding behavior is studied by displaying the propagation levels, delays, and critical users. The following preference derives from not only the usage habits but also underlying reasons such as personalities and social moralities that is worthy of future research.
Micro-blogging services in China experienced rapid growth. It is believed that Sina Weibo, the most popular one in China, may exceed Twitter in the number of users due to the huge Chinese netizen base. However, there are few quantitative works on Sina Weibo and the difference between these two micro-blogging magnates. studied users' basic behaviors such as access ways, writing style, topics, and interest change. They analyzed more than 40 million micro-blogging activities but didn't involve following relations @cite_21 . studied the patterns of advertisement propagation in Sina Weibo @cite_11 . They extracted propagation features such as volume, topology, and time then used K-means clustering algorithm to group the messages.
@cite_21 @cite_11
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In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research. 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.