  Increasing evidence suggests that, similar to face-to-face communications,
human emotions also spread in online social media. However, the mechanisms
underlying this emotional contagion, for example, whether different feelings
spread in unlikely ways or how the spread of emotions relates to the social
network, is rarely investigated. Indeed, because of high costs and
spatio-temporal limitations, explorations of this topic are challenging using
conventional questionnaires or controlled experiments. Because they are
collection points for natural affective responses of massive individuals,
online social media sites offer an ideal proxy for tackling this issue from the
perspective of computational social science. In this paper, based on the
analysis of millions of tweets in Weibo, surprisingly, we find that anger is
more contagious than joy, indicating that it can spark more angry follow-up
tweets. Moreover, regarding dissemination in social networks, anger travels
easily along weaker ties than joy, meaning that it can infiltrate different
communities and break free of local traps because strangers share such content
more often. Through a simple diffusion model, we reveal that greater contagion
and weaker ties function cooperatively to speed up anger's spread. The
diffusion of real-world events with different dominant emotions provides
further testimony to the findings. To the best of our knowledge, this is the
first time that quantitative long-term evidence has been presented that reveals
a difference in the mechanism by which joy and anger are disseminated. Our
findings shed light on both personal anger management in human communications
and on controlling collective outrage in cyberspace.

Social and Information Networks;Physics and Society