This project uses large-scale, real-world datasets—such as online reviews—to understand how consumers navigate and make decisions in digital environments. The USOAR student will assist in collecting, cleaning, and analyzing big data to identify behavioral patterns and generate actionable insights.
How can we use large-scale data to better understand how people think, behave, and make decisions in complex environments? This project uses computational tools and big data methods to uncover patterns in human behavior—from how people search for information online to how they evaluate options and make choices in the real world.
We analyze massive datasets such as online reviews, search queries, and behavioral traces from digital platforms. The research combines tools from data science, natural language processing (NLP), and machine learning with behavioral theories from psychology and cognitive science. The goal is to build models that help explain and predict human decisions at scale.
This work is part of a broader effort to bridge engineering and behavioral science, offering students the chance to work on high-impact, interdisciplinary questions. You will engage in end-to-end data science: from web scraping and data wrangling to exploratory analysis and computational modeling. Projects may also involve developing algorithms to quantify consumer sentiment, attention, or memory-related behavior using unstructured data.
If you're curious about how people behave in digital systems and want to apply technical skills to behavioral research, this project offers a unique and intellectually rewarding opportunity.
- Proficiency in programming (e.g., through coursework, projects, or self-study)
- Comfort with data manipulation and analysis (e.g., pandas, NumPy, tidyverse)
- Interest in human behavior, cognition, or behavioral data science
- Experience with web scraping, APIs, or NLP tools is a plus but not required
- How to collect, clean, and analyze large-scale naturalistic datasets
- Applications of natural language processing (NLP) and machine learning in human behavior research
- Quantitative modeling of decision processes
- How to interpret and communicate insights from data in an interdisciplinary context