Understanding Microtargeting Pattern on Social Media

Abstract

The landscape of social media is highly dynamic, with users generating and consuming a diverse range of content. 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; while it enhances the relevance and efficiency of targeted content, it also poses challenges. There is the risk of influencing user behavior and perceptions, fostering echo chambers and polarization. Understanding these impacts is crucial for promoting healthy public discourse in the digital age and maintaining a cohesive society. My work focuses on developing computational frameworks for better understanding of microtargeting and activity patterns on social media. To analyze the impacts of microtargeting, understanding messaging from both the sender’s and recipient’s perspectives is essential. For the sender, we need to know what are their motivations. For the recipient, we need to know something about their demographic properties and interests, according to which we hypothesize that messaging would change.

A significant challenge lies in understanding the messaging and how it changes depending on the targeted user groups. Another challenge arises when we do not know who the users are and what their motivations are for engaging with content. I address the challenges by developing computational approaches for (1) characterizing user types and their motivations for engaging with content, (2) analyzing the messaging based on topics relevant to the users and their responses to it, and (3) delving into the deeper understanding of the themes and arguments involved in the content.