Similarity in cognitive complexity and attraction to friends and lovers: Experimental and correlational studies

Abstract OnlyTwo studies are reported examining whether similarities in cognitive complexity foster different forms of interpersonal attraction. Study 1 provided an experimental test of the hypothesis that perceivers would be more attracted to targets with similar levels of complexity than to targets with dissimilar levels of complexity. Participants read interpersonal impressions reflecting low and high levels of cognitive complexity and completed 3 assessments of attraction (social, task, and intellectual) to the source of the impressions. As predicted, there were significant interactions between perceiver complexity and target complexity such that high-complexity perceivers were more attracted to high-complexity targets than were low-complexity perceivers, whereas low-complexity perceivers were more attracted to low-complexity targets than were high-complexity perceivers. Unexpectedly, however, low-complexity perceivers were more attracted to a high-complexity target than a low-complexity target. Study 2 examined the effects of similarities in cognitive complexity on attraction among 126 pairs of dating partners. Partners having similar levels of cognitive complexity expressed significantly greater intellectual attraction to one another than partners having dissimilar levels of cognitive complexity

Word usage mirrors community structure in the online social network Twitter

© 2013 Bryden et al. Background: Language has functions that transcend the transmission of information and varies with social context. To find out how language and social network structure interlink, we studied communication on Twitter, a broadly-used online messaging service. Results: We show that the network emerging from user communication can be structured into a hierarchy of communities, and that the frequencies of words used within those communities closely replicate this pattern. Consequently, communities can be characterised by their most significantly used words. The words used by an individual user, in turn, can be used to predict the community of which that user is a member. Conclusions: This indicates a relationship between human language and social networks, and suggests that the study of online communication offers vast potential for understanding the fabric of human society. Our approach can be used for enriching community detection with word analysis, which provides the ability to automate the classification of communities in social networks and identify emerging social groups