An Empirical Analysis of Intervention Strategies' Effectiveness for Countering Misinformation Amplification by Recommendation Algorithms

Social network platforms connect people worldwide, facilitating communication, information sharing, and personal/professional networking. They use recommendation algorithms to personalize content and enhance user experiences. However, these algorithms can unintentionally amplify misinformation by prioritizing engagement over accuracy. For instance, recent works suggest that popularity-based and network-based recommendation algorithms contribute the most to misinformation diffusion. In our study, we present an exploration on two Twitter datasets to understand the impact of intervention techniques on combating misinformation amplification initiated by recommendation algorithms. We simulate various scenarios and evaluate the effectiveness of intervention strategies in social sciences such as Virality Circuit Breakers and accuracy nudges. Our findings highlight that these intervention strategies are generally successful when applied on top of collaborative filtering and content-based recommendation algorithms, while having different levels of effectiveness depending on the number of users keen to spread fake news present in the dataset.