Algorithmic Curation in Facebook: An Investigation into the role of AI in Forming Political Polarization and Misinformation in Pakistan

Abstract

This research paper investigates the impact of AI Algorithmic curation and filter bubbles on the spread of misinformation and political polarization in Pakistan. It also highlights the recommendation system of Facebook and its role in exposing users to content based on selective exposure. The role of AI and recommendation algorithm curation in spreading misinformation, and deepening polarized political views have raised concerns. The AI Social Media Algorithmic Curation crafts a system based on personalization that improves user experience on the platform and maximizes engagement. However, this technique is also deepening polarization in the society by reinforcing pre-existing beliefs and limiting dissenting viewpoints. This multidisciplinary study applies quantitative techniques by employing Contingency Tables on the available and collected data to comprehend and reveal the underlying complex dynamics at play. The method systematically analyzes the data set of posts and the public’s view via a survey. The findings highlight the bias patterns in FB’s algorithm, user engagement data, and the role of AI in deepening polarization. There needs to be more transparency in the working of algorithmic curation and Meta should introduce features where users have more control over their feeds allowing diverse viewpoints.