Exposing individual differences through network topology

Social animals, including humans, have a broad range of personality traits, which can be used to predict individual behavioral responses and decisions. Current methods to quantify individual personality traits in humans rely on self-report questionnaires, which require time and effort to collect and rely on active cooperation. However, personality differences naturally manifest in social interactions such as online social networks. Here, we demonstrate that the topology of an online social network can be used to characterize the personality traits of its members. We analyzed the directed social graph formed by the users of the LiveJournal (LJ) blogging platform. Individual users personality traits, inferred from their self-reported domains of interest (DOIs), were associated with their network measures. Empirical clustering of DOIs by topological similarity exposed two main self-emergent DOI groups that were in alignment with the personality meta-traits plasticity and stability. Closeness, a global topological measure of network centrality, was significantly higher for bloggers associated with plasticity (vs. stability). A local network motif (a triad of 3 connected bloggers) that correlated with closeness also separated the personality meta-traits. Finally, topology-based classification of DOIs (without analyzing the content of the blogs) attained > 70% accuracy (average AUC of the test-set). These results indicate that personality traits are evident and detectable in network topology. This has serious implications for user privacy. But, if used responsibly, network identification of personality traits could aid in early identification of health-related risks, at the population level.