This study extends the analysis of the subreddit Desabafos by focusing on the temporal dynamics, predictive modeling, and the relationship between sentiment and user engagement in a large-scale Brazilian Portuguese online community. Using weekly and monthly time series derived from posts and comments, the research investigates the evolution of content volume, engagement patterns, and emotional distributions across the main flairs “Desabafo,” “Relacionamento,” and “Dia a Dia.” Trend and seasonal decomposition techniques were applied to identify long-term growth patterns and recurring seasonal behaviors. Univariate forecasting models, including ARIMA and Prophet, were employed to predict posting volume, engagement, and sentiment proportions, with Prophet achieving superior predictive performance. Additionally, multivariate time series models were used to evaluate the influence of sentiment on engagement. The results indicate strong temporal trends and seasonal effects, moderate predictive performance for engagement, higher accuracy in sentiment forecasting, and limited but context-dependent influence of sentiment on user engagement. These findings contribute to a deeper understanding of collective emotional expression and interaction dynamics in anonymous online environments.