Recent years have witnessed the tremendous growth of the online social media.
In China, Weibo, a Twitter-like service, has attracted more than 500 million
users in less than four years. Connected by online social ties, different users
influence each other emotionally. We find the correlation of anger among users
is significantly higher than that of joy, which indicates that angry emotion
could spread more quickly and broadly in the network. While the correlation of
sadness is surprisingly low and highly fluctuated. Moreover, there is a
stronger sentiment correlation between a pair of users if they share more
interactions. And users with larger number of friends posses more significant
sentiment influence to their neighborhoods. Our findings could provide insights
for modeling sentiment influence and propagation in online social networks.

该研究分析了微博中用户间的情感传播及影响，发现愤怒情绪在网络中传播更迅速和广泛，而悲伤情绪的相关性极低且波动较大；同时，若两个用户之间互动越多，则他们的情感相关性越强，拥有更多好友的用户对其邻居的情感影响更显著，这些研究发现为在线社交网络中情感影响及传播模型建立提供了启示。

愤怒比快乐更具影响力：微博情感相关性

Anger is More Influential Than Joy: Sentiment Correlation in Weibo

Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.

本文研究了在线社交媒体上的用户行为和内容推荐，以新闻聚合平台 Digg 为例，提出了一种随机模型用于区分内容的可见性和有趣性对用户的影响，并预测了用户对内容的兴趣，为网站内容设计提供了参考。