Understanding Microtargeting Pattern on Social Media

Date: Feb 12, 11:45am-12:45pm

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.

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 information 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. The initial phase of my research focuses on comprehensively understanding users and their underlying motivations, whether practitioner-based or promotional. Step beyond that, assuming the identification of the involved parties, my work aims to characterize the messaging and explore how it adapts based on various targeted demographic groups. In this talk, I will present the 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. Finally, I will outline future directions for utilizing advanced AI technologies to bridge the gap between societal needs and technological solutions so that AI can serve as a catalyst for understanding and improving human experiences within diverse social contexts.

Biographical Sketch: Tunazzina Islam is a Ph.D. candidate in the Department of Computer Science at Purdue University, advised by Dr. Dan Goldwasser. Her research vision is to understand microtargeting and activity patterns on social media by developing computational approaches and frameworks blending computational social science (CSS), natural language processing (NLP), and artificial intelligence (AI). Her work has been recognized by her publications in prominent conferences including AAAI ICWSM, NAACL, ACL, AIES, ACM WebSci, IEEE BigData, and awards (Purdue Graduate School Summer Research Grant: 3 times). Her Ph.D. thesis proposal has been accepted at AAAI-25 Doctoral Consortium. Beyond research, she has 9 years of teaching experience in various roles such as teaching assistant, guest lecturer, mentor, and trainer. For her teaching contributions, she received the Graduate Teaching
Award from Purdue CS Department. To support CSS research community, she became ICWSM ambassador, introducing the conference to the interested researchers and individuals from underrepresented groups. She has served as Tutorial Co-chair of ICWSM 2025 and Associate Chair for CSCW 2024, CSCW 2025. Additionally, she has been a reviewer for numerous NLP, CSS, HCI, and AI conferences and workshops since 2020. To foster the Data Science research community, she organized a tutorial on “Analyzing Microtargeting on Social Media” at 2024 Academic Data Science Alliance (ADSA) Annual Meeting. She also served as a Vice President of the Computer Science Graduate Student Association (CSGSA), Purdue University, from 2022 to 2023.

Location and Zoom link: Zoom only at https://fsu.zoom.us/j/7121492101?omn=94822454956