About Me
ujeong1@asu.edu
My name is Ujun Jeong. I am a 5th year Ph.D. student at Arizona State University. I am part of the Data Mining and Machine Learning (DMML) Lab, under the guidance of Dr. Huan Liu.
My research in digital sociology focuses on modeling user behavior, such as how interactions shape information flow, community formation, and platform dynamics. I develop computational methods to uncover higher-order and implicit relations in social networks using graph and hypergraph learning. I also use econometric techniques to examine how platform design and cross-platform relations influence user behavior through incentives and responses to external shocks. By combining machine learning with sociological theory, my work aims to derive actionable insights from complex social systems and support the design of more engaging and safer digital environments.
Before starting my Ph.D., I was honored with the distinction of being the youngest captain ever to serve in the Korean Air Force with a specialization in information and communication. During my Master's degree, I studied Computer Science with a focus on Natural Language Processing at Kyungpook National University. For my Bachelor's degree, I majored in Computer Science & Engineering at Sejong University with full tuition with stipend from the Scholarship Foundation for Future Leaders.
News
• May 2025: I will join Amazon's Buyer Risk Prevention team as an applied scientist intern during summer.
• Nov 2024: Science Magazine quoted my interview at "Like ‘old Twitter’: The scientific community finds a new home on Bluesky"
• Nov 2024: Successfully finished my internship at Amazon as applied scientist intern with Private Brand Intelligence team
• Sept 2024: Research Professional News quoted my research at “Academics Struggling to Replicate X Community Elsewhere”
Publications
Google Scholar
• "BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social"
[PDF]
[IEEE Data Portal]
[Github]
In IEEE Data Descriptions (IEEE DATA 2024).
• "User Migration across Multiple Social Media Platforms"
[PDF]
In SIAM International Conference on Data Mining (SDM 2024).
• "Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study"
[PDF]
In International AAAI Conference on Web and Social Media (ICWSM 2024).
• "Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks"
[PDF]
In IEEE International Conference on Big Data (IEEE Big Data 2022).
• "Classifying COVID-19 related Meta Ads using Discourse Representation through a Hypergraph"
[PDF]
In International Conference on Social Computing, Behavioral-Cultural Modeling, Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2022).
• "CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods"
[PDF]
In International Conference on Information and Knowledge Management (CIKM 2021), demo track.
• "FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements"
[PDF]
In International Conference on Social Computing, Behavioral-Cultural Modeling, Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2021), demo track.
• "Fake News Detection Using External Information on Social Media"
In Proceedings of the Korea Computer Congress (KCC 2018)
Preprints & Under Review
• "FediverseSharing: A Novel Dataset on Cross-Platform Interaction Dynamics between Threads and Mastodon Users"
[PDF]
• "Large Language Models for Causal Relations Extraction in Social Discourse: Insights from Billion-Dollar Disaster Events"
• "Navigating Decentralized Online Social Networks: An Overview of Technical and Societal Challenges in Architectural Choices"
[PDF]
• "Surveying the Terrains of Social Media Platforms through the Lens of User Migration Pattern"
[Website]
In IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2024), doctoral forum.
• "On the Fragility of LLM Agents’ Consensus with Network-Enabled Opinion Dynamics"