The Behavioral Data Science lab is a research group in the UTS Data Science Institute at the University of Technology Sydney, Australia.
Our research aims to make sense of large amounts of behavioral data, to understand online behavior and its offline effects. We develop core methods in machine learning and optimization in order to distill heaps of raw data into meaningful insights in areas including
ICWSM 2022 paper: “Slipping to the Extreme: A Mixed-Method to Explain How Extreme Opinions Infiltrate Online Discussions”
ECREA 2021 presentation: “Discovering the Strategies and Promotion Schedules of Coordinated Disinformation via Hawkes Intensity Processes”
More than a quarter of a century since the first commercial use of the online world, its growth is now slowing down in some key categories.
Summary: We build a machine learning-based Job Transitions Recommender System that can accurately predict the probability of transitioning between occupations. We showcase the system for workers forced to transition between jobs. The system is based on a novel data-driven method to measure the similarity between occupations based on their underlying skill profiles and real-time job ads. We also build a leading indicator of Artificial Intelligence adoption in Australian industries, outlining gaps, opportunities, and trends.