Behavioral Data Science lab

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

  • social and information network analysis,
  • information diffusion across social networks,
  • mis- and dis-information spreading,
  • tomorrow's labour markets and labour transitions,
  • machine learning,
  • computational social science,
  • data mining, and
  • natural language processing

The Behavioral Data Science lab has a PhD opportunity available. If you are interested in working with us, please get in touch.

Recent News

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Recent Posts

We spent six years scouring billions of links, and found the web is both expanding and shrinking

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.

Job Transitions in a Time of Automation and Labour Market Crises

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.

User Analysis on reshare cascades about COVID-19

We demonstrate in this blog post a tutorial on applying the tools for analyzing online information diffusions about Twitter users, birdspotter and evently. Dataset In this tutorial, we apply two tools for analyzing Twitter users, on a COVID-19 retweet dataset. The dataset is curated by Chen, et al.Β One can obtain a copy of the tweet IDs from their project. We only use the 31st of Janury sample of the whole dataset for demonstration purpose.