One well-known feature of social networks is that similar people tend to attract each other: birds of a feather flock together.
So an interesting question is whether these similarities cause people to behave in the same way online — whether it might lead to flocking or herding behavior, for example.
Today, we get an interesting insight into this phenomena thanks to the work of Rui Fan and colleagues at Beihang University in China. They have compared the way that tweets labeled with specific emotions influence other people on the network.
And their conclusion is surprising. They say the results clearly show that anger is more influential than other emotions such as joy or sadness, a finding that could have significant implications for our understanding of the way information spreads through social networks.
The researchers got their data from Weibo, a Twitter-like service that has become hugely popular in China. In just four years, it has attracted more than 500 million users who post about 100 million messages a day.
During six months in 2010, the group collected some 70 million tweets from 200,000 users and constructed a social network in which users are linked if they mutually interact by sending messages to each other or retweeting each other, for example.
To ensure they only studied people who were strongly connected, they only included people who had more than 30 interactions during the test period.
Next, they determined the sentiment of each tweet in their database by analyzing the emoticons they contained. They divided these into four categories, expressing joy, sadness, anger or disgust.
Finally, the group studied the way sentiments spread through the network. For example, if one person sent an angry tweet, how likely was it that a recipient would also send an angry message, and how likely was it that the recipient of this message would pass on the same sentiment, and so on?
The results were something of a surprise. When it comes to sadness and disgust, Rui and his colleagues found very little correlation between users.
Sadness and disgust do not easily spread through the network in this way. They found a higher correlation among users who tweeted joyful messages.
But the highest correlation by far was among angry users. Rui said anger strongly influences the neighborhood in which it appears, spreading on average by about three degrees. “Anger has a surprisingly higher correlation than other emotions,” the researchers said.
That has significant implications, not least of which is that anger is more likely to spread quickly and broadly across a network.
Indeed, the researchers confirmed this by studying the content of many of the angry tweets they had collected. They found that two kinds of events seem to trigger angry messages.
The first is conflicts between China and foreign countries, such as the military activities of the U.S. and South Korea in the Yellow Sea and a collision in September 2010 between a Chinese and Japanese ship.
The second is domestic social problems like food security, government bribery and the demolition of homes for resettlement; all hot topics in China. “This can explain why the events related to social problems propagate extremely fast in Weibo,” Rui and his team said.
Of course, it would be interesting to see whether the same effect can be observed in western networks such as Twitter. That should be relatively straightforward to find given the growing interest in sentiment analysis and the increasingly effective tools available to carry it out.
The moral of the story is that, when it comes to the spread of information, anger is more powerful than other emotions.
So if you want to spread your message, let that inner rage out.