Happiness is assortative in online social networks

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".Comment: 17 pages, 9 figure

Homophilic network decomposition: a community-centric analysis of online social services

In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks—Skype, LastFM and Google+—and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+

Social networks in COVID-19 America: Americans remotely together but politically apart

The COVID-19 pandemic has presented a social dilemma; "social distancing" was required to stop the spread of disease, but close social contacts were needed more than ever to collectively overcome the unprecedented challenges of the crisis. How did Americans mobilize their social ties in response to the pandemic? Drawing from a nation-wide daily online survey of 36,345 Americans from April 2020 through April 2021, we examine the characteristics of Americans' core networks within which people discuss "important matters." Comparing the COVID-19 networks to those previously collected in eight national core network surveys from 1985 to 2016, we observe remarkable stability in the size and relationship composition of core networks during COVID-19. In contrast to the robust nature of core networks, we discover a significant rise in racial homophily among kin ties, and political homophily among non-kin ties. Simultaneously, our study reveals a significant surge in the adoption of remote communication technology to connect with individuals who are geographically distant. We demonstrate that the changing mode of communication contributes to increases in racial and political homophily. These results suggest that the COVID-19 pandemic may bring people remotely together but only with the like-minded, deepening social divides in American society