Understanding Microtargeting Pattern on Social Media

Abstract

We now live in a world where we can reach people directly through social media, without relying on traditional media such as television and radio. On the other hand, social media platforms collect vast amounts of data and create very specific profiles of different users through targeted advertising. Various interest groups, including politicians, advertisers, and stakeholders, utilize these platforms to target potential users to advance their interests by adapting their messaging. This process, known as microtargeting, relies on data-driven techniques that exploit the rich information collected by social networks about their users. Microtargeting is a double-edged sword. It enhances the relevance and efficiency of targeted content, can influence people to take action based on personal beliefs. This could be great, increasing the relevance based on users to help guide people in making better health decisions and offering them opportunities for career growth. On the other hand, it can influence people to make decisions against their own interests, foster echo chambers, and increase polarization. My research is motivated by the fact that some of these risks can be mitigated by providing transparency, identifying conflicting or harmful messaging choices, and indicating bias introduced in messaging in a nuanced way. I provide computational frameworks to analyze microtargeting patterns, which will help policymakers make better decisions. This is crucial for promoting healthy public discourse in the digital age and maintaining a cohesive society.