Analysis of Algorithmic Content Curation for user Engagement and Information Diversity on Social Media

Abstract

The ubiquity of social networking sites (SNS) has transformed the way individuals connect and communicate in the digital age. This research     investigates the impact of algorithmic content curation on user engagement and information diversity within a popular social networking platform. The        proposed system uses a mixed-methods approach, combining quantitative   analysis of user interactions with qualitative insights gathered through user  surveys and interviews. The study focused on a sample of 1,000 active users, tracking their interactions with content over a six-month period. Additionally, qualitative data was collected through in-depth interviews with a subset of users to understand their perceptions and experiences related to algorithmic content recommendations. Key findings indicate a significant correlation between      algorithmic content curation and user engagement metrics. However, the study also reveals a potential downside in terms of information homogenization, as users tend to be exposed to a narrower range of content aligned with their      existing preferences. This phenomenon raises concerns about the potential   formation of echo chambers and the impact on the diversity of opinions and   information dissemination. The implications of these findings extend to the   design of algorithmic recommendation systems on social networking sites and their broader societal effects. As social media platforms play an increasingly central role in shaping public discourse, understanding and addressing the   challenges posed by algorithmic content curation is crucial for fostering a more informed and diverse digital public sphere. This research contributes to the growing body of literature on the social dynamics of online platforms and    provides valuable insights for both scholars and practitioners seeking to balance personalized content recommendations with the need for diverse and inclusive online spaces.