After asking every member in the room, a normal-looking nonmember seal approached me. "Doesn't he have really small eyes?" recalled another user. "Oh, I've heard that he has night black fur.like, darker than you can choose," said one Arctic Wolf. All I knew for sure was that someone was taking FMAN's place as the most feared AJ player, and that someone had become known as the Lost Jammer.Įveryone in Jamaa Township knew about the Lost Jammer, but not a single person seemed to know his username. I couldn't tell what was real and what was fake. Others said he was an AJ staff member who was fired and used inside information to access others' accounts. Some claimed he was formerly a regular Jammer, who was scammed and then devoted the rest of his life to hacking Animal Jam to obtain account information. I was very confused, as all of my buddies told me slightly different stories about a "new FMAN". Suddenly, in late May 2015, everything changed new rumours had started. The game had gotten old for some experienced users. Some could say that Jammers were bored of everyday life on AJ there wasn't anything exciting or new to discover. Jammers had been about their business for quite some time without fear of getting hacked, minus the occasional fake FMAN users popping up around Jamaa. It had been two years since the FMAN rumours began on Animal Jam. Face-to-face interactions in social groups are a central aspect of human social lives.Note: One name has been changed in this story. Although the composition of such groups has received ample attention in various fields-e.g., sociology, social psychology, management, and educational science-their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals’ interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms-such as homophily, transitivity, and popularity-to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting. Real-life social interactions occur in continuous time and are driven by complex mechanisms. Each interaction is not only affected by the characteristics of individuals or the environmental context but also by the history of interactions. The relational event framework provides a flexible approach to studying the mechanisms that drive how a sequence of social interactions evolves over time. This paper presents an introduction of this new statistical framework and two of its extensions for psychological researchers. The relational event framework is illustrated with an exemplary study on social interactions between freshmen students at the start of their new studies. We show how the framework can be used to study: (a) which predictors are important drivers of social interactions between freshmen students who start interacting at zero acquaintance (b) how the effects of predictors change over time as acquaintance increases and (c) the dynamics between the different settings in which students interact. Findings show that patterns of interaction developed early in the freshmen student network and remained relatively stable over time. Furthermore, clusters of interacting students formed quickly, and predominantly within a specific setting for interaction. Extraversion predicted rates of social interaction, and this effect was particularly pronounced on the weekends. These results illustrate how the relational event framework and its extensions can lead to new insights on social interactions and how they are affected both by the interacting individuals and the dynamic social environment. More and more data are being collected using combined active (e.g., surveys) and passive (e.g., smartphone sensors) ambulatory assessment methods. Fine-grained temporal data, such as smartphone sensor data, allow gaining new insights into the dynamics of social interactions in day-to-day life and how these are associated with psychosocial phenomena – such as loneliness. So far, however, smartphone sensor data have often been aggregated over time, thus, not doing justice to the fine-grained temporality of these data. In this article, we demonstrate how time-stamped sensor data of social interactions can be modeled with multistate survival models.
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