Digital social networks have become increasingly popular, offering unprecedented opportunities to analyze human behavior in computer-mediated contexts. This research investigates how social network users can be clustered based on their personality traits, digital footprints, and demographic data. We studied users of a wedding planning social network platform, providing a unique context where users are actively engaged in organizing a significant life event. Unlike previous studies that primarily focus on active digital footprints from major social networks, we explore both active and passive digital traces alongside personality traits collected through a brief self-report questionnaire based on the Big Five Personality model. We analyzed data from 149 users, combining behavioral data (450+ dimensions) with personality traits collected through the Reduced Scale of Big Five Personality Factors. Using unsupervised machine learning techniques (K-Means, Spectral Clustering, Agglomerative Clustering, and K-medoids), we identified distinct user clusters and examined their characteristics. Our findings reveal meaningful patterns between personality traits and online behavior: users with higher conscientiousness scores showed more meticulous wedding planning patterns, while those with lower openness to experience demonstrated less content exploration. Despite the limited sample size due to external factors (COVID-19 pandemic), our study presents a novel analysis of a Portuguese-language social network from Brazil (Wedy), uniquely combining self-reported personality traits with extensive active and passive digital footprints and demographic data, offering preliminary insights into the relationship between personality and online behavior during significant life events in a specific and under-explored context.