  Increasing evidence suggests that, similar to face-to-face communications,
human emotions also spread in online social media. However, the mechanisms
underlying this emotion 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
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
weaker ties speed up anger by applying both propagation velocity and coverage
metrics. 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. With the extensive
proliferation of weak ties in booming social media, our results imply that the
contagion of anger could be profoundly strengthened to globalize its negative
impact.
