Digital platforms continuously shape user behavior through strategic communication and algorithmic personalization, influencing how users engage with content, make decisions, and interact within online communities. Despite their widespread use, these platforms face critical challenges: moderating online toxicity without discouraging content creators, balancing engagement from focal users while attracting potential new users, and optimizing communication frequency without risking user drop-off. My dissertation addresses these challenges through three insightful empirical studies that integrate informational signaling, behavioral nudging, and personalized targeting. The first study, leveraging observational data from Truth Social alongside field experiments on X, examines how users' strategic use of informational cues, particularly hashtags, influences audience composition and proactively reduces online toxicity. The second study evaluates behavioral nudging interventions on a content-sharing platform, exploring trade-offs between fostering increased content consumption time within the platform and attracting new users through content shared externally. In the third study, I analyze large-scale experimental data from Netflix to examine how personalized messaging volumes can more effectively balance user engagement and retention compared to a default uniform messaging strategy. Collectively, these studies contribute to a deeper understanding of the interplay between user behavior, platform algorithms, and strategic interventions. My work provides actionable insights for content creators, marketers, and platform designers to influence user experiences, optimize engagement strategies, and design more effective and healthier digital platforms.