Happiness is assortative in online social networks

TL;DR

This study demonstrates that happiness levels, as measured from Twitter data, tend to be similar among connected users, indicating that online social networks exhibit assortative mixing for subjective well-being, similar to real-world social networks.

Contribution

First to show that subjective well-being is assortative in online social networks, revealing social mechanisms at play in digital environments.

Findings

Happiness is correlated among connected Twitter users.

Assortative mixing of SWB occurs in online networks.

Implications for understanding sentiment spread online.

Abstract

Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that general happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6 month record of their individual tweets, is indeed assortative across the Twitter social network. To our knowledge this is the first result that shows assortative mixing in online networks at the level of SWB. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important instrument in better understanding how both positive and negative sentiments spread through online social ties. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life".