This dissertation addresses a novel approach to assessing users' interaction tendencies on social media as a basis for personalized interventions that can make the truth louder and mitigate the spread of misinformation. This research leverages users' high and low interaction tendencies to amplify truth by increasing users' interactions with verified posts and decreasing their interactions with unverified posts. For designing personalized interaction-focused interventions, this dissertation presents an Active-Passive (AP) framework and three principles of social media interactions to make the truth louder on social media. This dissertation presents a study including tasks and questionnaires to investigate users' differences in the Active-Passive (AP) framework for utilizing platforms' basic interaction functionalities, such as like, comment, or share buttons. The results show that users use the interaction functionalities differently due to their interaction tendencies; users with high interaction tendencies use more interaction functionalities, and users with low interaction tendencies use less. This dissertation presents an analysis of participants' responses to the design principles and investigates users' additional sharing functionality usage and preference for platform-based incentives. The results show that users with lower interaction tendencies share verified information more when they receive additional interaction support. Furthermore, due to the interaction tendencies, users exhibit opposite preferences for platform-based incentives that can encourage their participation in making the truth louder. Users with high interaction tendencies prefer incentives that highlight their presence on the platform, and users with low interaction tendencies favor incentives that can educate them about the impact of their participation on their friends and community. This dissertation concludes with a discussion on personalized interaction-focused interventions and provides directions for future research.