Characteristics of online user-generated text predict the emotional intelligence of individuals.

Emotional intelligence is a well-established indicator of performance and the ability to maintain successful social relationships. Moreover, it is potentially an important factor in social dynamics occurring on large digital platforms, e.g., opinion polarization, social conflict, and social influence. Users publicly exchange enormous amounts of text on digital platforms, which can potentially be used to extract real-life insights. Yet, currently, the prevalent approach to measuring emotional intelligence uses mainly self-report surveys and tasks-considerably limiting the feasibility of real-life large-scale studies. We analyze the online public texts of users, who also completed emotional intelligence measures, to find that characteristics of online public texts can be used to predict emotional intelligence at a level like that of commonly used psychometric indicators (e.g., SATs) to predict real-life outcomes. For example, we find that high emotional intelligence individuals consistently use more positive-affect language, less negative-affect language and use more social-oriented language than low emotional intelligence individuals. Our findings provide insight into the role of personality on digital platforms and open the possibility of studying emotional intelligence in large and diverse real-life data. To support the use of online public text as a tool to research emotional intelligence, we provide an anonymized version of the data.

Sustainable online communities exhibit distinct hierarchical structures across scales of size.

As humans, we are uniquely competent at incorporating ourselves into groups that scale up from a few members to millions of individuals to engage in joint activities in social circles of varying sizes. Yet, the question of how a group's survival depends on its social structure is not well understood. In an analysis of more than 10 122 real-life online communities (with a total of 134 147 members) hosted by a leading platform over periods of more than a decade, we observe a prominent structural difference between stable and unstable communities, enabling the prediction of sustainability up to a decade ahead. We find that communities that fail to maintain a typical hierarchical social structure that preserves cohesiveness across size scales do not survive, while communities that exhibit such balance prevail. This difference is observable in as early as the first 30 days of a community's lifetime, enabling prediction of community sustainability up to 10 years in the future. We theorize that communities comprising distinct social structures that balance global and local factors across scales of sizes are more likely to maintain sustainability.

Using free association networks to extract characteristic patterns of affect dynamics.

The dynamics of human affect in day-to-day life are an intrinsic part of human behaviour. Yet, it is difficult to observe and objectively measure how affect evolves over time with sufficient resolution. Here, we suggest an approach that combines free association networks with affect mapping, to gain insight into basic patterns of affect dynamics. This approach exploits the established connection in the literature between association networks and behaviour. Using extant rich data, we find consistent patterns of the dynamics of the valence and arousal dimensions of affect. First, we find that the individuals represented by the data tend to feel a constant pull towards an affect-neutral global equilibrium point in the valence-arousal space. The farther the affect is from that point, the stronger the pull. We find that the drift of affect exhibits high inertia, i.e. is slow-changing, but with occasional discontinuous jumps of valence. We further find that, under certain conditions, another metastable equilibrium point emerges on the network, but one which represents a much more negative and agitated state of affect. Finally, we demonstrate how the affect-coded association network can be used to identify useful or harmful trajectories of associative thoughts that otherwise are hard to extract.

Emergence of online communities: Empirical evidence and theory.

Online communities, which have become an integral part of the day-to-day life of people and organizations, exhibit much diversity in both size and activity level; some communities grow to a massive scale and thrive, whereas others remain small, and even wither. In spite of the important role of these proliferating communities, there is limited empirical evidence that identifies the dominant factors underlying their dynamics. Using data collected from seven large online platforms, we observe a relationship between online community size and its activity which generally repeats itself across platforms: First, in most platforms, three distinct activity regimes exist-one of low-activity and two of high-activity. Further, we find a sharp activity phase transition at a critical community size that marks the shift between the first and the second regime in six out of the seven online platforms. Essentially, we argue that it is around this critical size that sustainable interactive communities emerge. The third activity regime occurs above a higher characteristic size in which community activity reaches and remains at a constant and higher level. We find that there is variance in the steepness of the slope of the second regime, that leads to the third regime of saturation, but that the third regime is exhibited in six of the seven online platforms. We propose that the sharp activity phase transition and the regime structure stem from the branching property of online interactions.

Centralized nucleation in online networks leads to high social inequality.

Even though the heterogeneity of centrality in social networks is well documented, its role and effect on network stability in real life remains unclear. The literature roughly suggests that network structure is such that networks have an "inner" highly-connected nucleus and, in contrast, sparse outer shells. But to what extent is the existence of this nucleus crucial for the survival of a network? To what extent is the outer shells' much larger population essential to the longevity of the network? Furthermore, as a network grows and forms, theoretically speaking, network structure should be dependent on the patterns of change of degree centrality, i.e., social mobility between centrality shells. What is the role of social mobility in the formation of the nucleus-to-periphery profile, and is it related to network lifetime? Here, we explore these questions using data collected covering over a decade of activity from more than 10, 000 networked communities, with more than 134,000 users. We find that: (i) social mobility is, on average, negative but that, (ii) the higher the social mobility of the members of the network, the more stable and long-living the network is. Further, (iii) the network is, indeed, composed of two phases - a large but ephemeral sparsely connected "cloud" of actors, that nucleates around a highly stable nucleus of users. Lastly, (iv) networked communities which maintain a specific nucleus-to-periphery ratio η, i.e., a ratio of the size of the nucleus to periphery of around η = 1 4 , have a greater chance of survival. We find that deviations from this nucleus-to-periphery ratio predict a collapse of network activity, especially in the case of younger communities.