Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. We also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, we construct a network of cultures and identify the most influential cultures according to this network.

Despite the undisputed role of emotions in teamwork, not much is known about the make-up of emotions in online collaboration. Publicly available repositories of collaboration data, such as Wikipedia editor discussions, now enable the large-scale study of affect and dialogue in peer production. We investigate the established Wikipedia community and focus on how emotion and dialogue differ depending on the status, gender, and the communication network of the editors who have written at least 100 comments on the English Wikipedia's article talk pages. Emotions are quantified using a word-based approach comparing the results of two predefined lexicon-based methods: LIWC and SentiStrength. We find that administrators maintain a rather neutral, impersonal tone, while regular editors are more emotional and relationship-oriented, that is, they use language to form and maintain connections to other editors. A persistent gender difference is that female contributors communicate in a manner that promotes social affiliation and emotional connection more than male editors, irrespective of their status in the community. Female regular editors are the most relationship-oriented, whereas male administrators are the least relationship-focused. Finally, emotional and linguistic homophily is prevalent: editors tend to interact with other editors having similar emotional styles (e.g., editors expressing more anger connect more with one another). Emotional expression and linguistic style in online collaboration differ substantially depending on the contributors' gender and status, and on the communication network. This should be taken into account when analyzing collaborative success, and may prove insightful to communities facing gender gap and stagnation in contributor acquisition and participation levels.

Gender homophily, or the preference for interaction with individuals of the same gender, has been observed in many contexts, especially during childhood and adolescence. In this study we investigate such phenomenon by analyzing the interactions of the ∼10 million users of Tuenti, a Spanish social networking service popular among teenagers. In dyadic relationships we find evidence of higher gender homophily for women. We also observe a preference of users with more friends to connect to the opposite gender. A particularly marked gender difference emerges in signing up for the social networking service and adding the first friends, and in the interactions by means of wall messages. In these contexts we find evidence of a strong homophily for women, and little or no homophily for men. By examining the gender composition of triangle motifs, we observe a marked tendency of users to group into gender homogeneous clusters, with a particularly high number of male-only triangles. We show that age plays an important role in this context, with a tendency to higher homophily for young teenagers in both dyadic and triadic relationships. Our findings have implications for addressing gender gap issues, understanding adolescent online behavior and technology adoption, and modeling social networks.