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In this study, we explore peer-interaction effects in online networks onspeaking skill development. In particular, we present an evidence for gradualbuildup of skills in a small-group setting that has not been reported in theliterature. We introduce a novel dataset of six online communities consistingof 158 participants focusing on improving their speaking skills. Theyvideo-record speeches for 5 prompts in 10 days and exchange comments andperformance-ratings with their peers. We ask (i) whether the participants'ratings are affected by their interaction patterns with peers, and (ii) whetherthere is any gradual buildup of speaking skills in the communities towardshomogeneity. To analyze the data, we employ tools from the emerging field ofGraph Signal Processing (GSP). GSP enjoys a distinction from Social NetworkAnalysis in that the latter is concerned primarily with the connectionstructures of graphs, while the former studies signals on top of graphs. Westudy the performance ratings of the participants as graph signals atopunderlying interaction topologies. Total variation analysis of the graphsignals show that the participants' rating differences decrease with time(slope=-0.04, p<0.01), while average ratings increase (slope=0.07,p<0.05)--thereby gradually building up the ratings towards community-widehomogeneity. We provide evidence for peer-influence through a predictionformulation. Our consensus-based prediction model outperforms baselinenetwork-agnostic regression models by about 23% in predicting performanceratings. This, in turn, shows that participants' ratings are affected by theirpeers' ratings and the associated interaction patterns, corroborating previousfindings. Then, we formulate a consensus-based diffusion model that capturesthese observations of peer-influence from our analyses.

Buildup of Speaking Skills in an Online Learning Community: A Network-Analytic Exploration