Walls-in-one: usage and temporal patterns in a social media aggregator

Abstract

The continual launches of new online social media that meet the most varied people’s needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people’s identity and behavior are often partial, incomplete and scattered in different “places”. To overcome this identity fragmentation and to give an all-around picture of people’s online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator.The analysis of social media usage shows that Alternion data reflect the novel trend of today’s users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users’ degree in five different social networks, we performed a rank correlation analysis on users’ degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals’ importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites.

Aging effects in Schelling segregation model

“…It is known that human interactions do not occur at a constant rate. They rather show a bursty character with a non-Poissonian inter-event time distribution that reflects a memory from past interactions [34][35][36][37][38][39] www.nature.com/scientificreports/ interactions or state updating. "Aging" is one form of memory effect on which the rate of interactions depends on the persistence time of an agent in a state, modifying the transition to a different state [40][41][42] .…”

Ordering dynamics and aging in the symmetrical threshold model

“…The introduction of aging is motivated by strong empirical evidence that human interactions do not occur at a constant rate and cannot be described using a Markovian assumption. Empirical studies have reported heavy-tail inter-event time distributions that reflect heterogeneous temporal activity patterns in social interactions [43][44][45][46][47].…”

Aging effects in Schelling Segregation model

“…This rate dependence on the persistence times accounts for the observation that human interactions do not occur at a constant rate. They rather show a bursty character with a non-Poissonian inter-event time distribution [37][38][39][40][41][42]. However, most social simulations, including simulations of variants of the Schelling model, implicitly assume a constant rate of interactions or state updating.…”