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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May 2022
Check-out the presentation video for our paper Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions, soon to be presented at ICWSM'22.
Apr 2022
📄 The paper “Skills Taught vs Skills Sought: Using Skills Analytics to Identify the Gaps between Curriculum and Job Markets”, together with Connected Intelligence Centre (CIC) colleagues Alireza Ahadi, Kirsty Kitto, Kaska Musial was accepted in Educational Data Mining 2022.
Feb 2022
Amelie Girard started today her PhD on how to bridge the gap between ML prediction, and explaining decisions (say in a marketing, consulting or HR context). Welcome, Amelie!
Feb 2022
Daniela Elia started today her PhD on augmenting skill and occupational ontologies with the demand on the employer's side. Welcome, Daniela!
Nov 2021
📄 The paper Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions by Quyu and Andrei and collaborators from the School of Communication @UTS just got accepted in 16th International Conference on Web and Social Media (ICWSM'22).

Recent Posts

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Quyu Kong, (edited by Marian-Andrei Rizoiu)

Dec 13, 2021 Research

Slipping to the Extreme: A Mixed-Method to Explain How Extreme Opinions Infiltrate Online Discussions

ICWSM 2022 paper: “Slipping to the Extreme: A Mixed-Method to Explain How Extreme Opinions Infiltrate Online Discussions”

Marian-Andrei Rizoiu

Sep 27, 2021 Presentation

Discovering coordinated disinformation via Hawkes processes

ECREA 2021 presentation: “Discovering the Strategies and Promotion Schedules of Coordinated Disinformation via Hawkes Intensity Processes”

Paul X. McCarthy, Marian-Andrei Rizoiu

Jul 2, 2021 Research

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.

Nik Dawson (edited by Marian-Andrei Rizoiu)

Feb 4, 2021 blogpost

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.