During adolescence, individuals place increasing emphasis on peer relations. Both positive and negative relationships contribute to adolescents’ academic performances and well-being. Adolescence is also a period when individuals actively engage in gossiping. Despite the various benefits brought by gossip such as information exchange, norm regulation and amusement, those who are labelled as “gossipers” tend to suffer from bad reputation and thus becoming less favored in friendship selection. Instead of focusing on evident gossiping behaviors, the current study takes an innovative approach of looking at gossip perception and the friendship and disliking relationship between perceived gossipers who are not necessarily real gossipers but are nominated when others were being asked “Who do you think talks you out with other classmates behind your back?” and the self-perceived targets. The research is conducted by applying Meta Analysis and Bayesian Multilevel Random Coefficients Analysis to 11 Hungarian classrooms of the RECENS project “Competition and Negative Networks” (2017) using Stochastic Actor Oriented Modelling (SAOM) in R. Results from Meta Analysis are in line with the expectation that gossip perception should make the self-perceived target less likely to befriend the perceived gossiper and more likely to dislike him/her. No evidence is found to support that having a shared perceived gossiper should bring two self-perceived targets closer or a self-perceived target should be more likely to dislike the friend of the perceived gossiper following Heider’s Balance Theory (1946). However, no gossip perception related effects are found statistically significant according to the results of the Bayesian approach. Overall, gossip perception is not powerful enough to affect adolescents’ attitude, whether positive or negative, towards one another.

<p>Social presence positively influences the motivation, satisfaction, retention, and learning outcomes of online students. Although it is crucial for successful online learning experiences, little work has thoroughly examined the evolution of social presence over time and the influence of social presence on peer interaction. In other words, if social presence can be learned by interacting with others. This three-article dissertation study elucidates this gap by answering the overarching question: How does online students' social presence evolve over time to shape their online learning behaviors? Using stochastic-actor oriented models to reflect the dependence among learners in online collaborative learning communities, this dissertation investigated how learners' social presence evolved in learner-learner interaction resulting in two empirical studies and one conceptual framework. The first study explored social presence through clickstream interaction (e.g., number of replies received/sent in an online discussion) of 382 learners enrolled in a Massive Open Online Course. Three key findings from the study were: 1) dropout rates could be lowered if social presence affordances are used purposefully; 2) adding social media characteristics to online discussion boards, for example, "like" buttons, inhibits conversational behavior, and eventually, decreases achievement of learning outcomes; and 3) the "rich-get-richer" effect also applies to social presence, reinforcing highly active students' behavior and risking inactive online students to experience isolation. The second study used peer-nomination data (i.e., asking students who they interact with) and a scale to investigate the spread of social presence perceptions in online networks of students over three consecutive courses (n = 197). Although there was no evidence of social influence, online learners who nominated more peers are more likely to report higher social presence perceptions over time. Students were not more likely to share with those who showed similar levels of social presence. The "rich-get-richer effect" was observed in the incoming nominations of learners. The third study is a conceptual framework that integrates network theory and the online learning literature into a new perspective to analyze learners' online behaviors and interactions under the light of social presence theory. The proposed framework includes four main steps: 1) interaction, 2) social presence alignment, 3) unit of analysis definition, and 3) network statistics and inferential analysis selection. The findings of this dissertation improve educational practice by identifying behaviors that harm online social presence and providing specific actions for online instructors and instructional designers to promote social presence in online learning.  </p>