Researcher Profile

 

  • Senior Lecturer in Computer Science at the University of Technology Sydney

  • Human actions online, human attention dynamics in the online environment, and online influence opinion polarisation

  • Stochastic behavioural modelling, applied statistics, artificial intelligence, and social data science

  • 1. Rizoiu, M.-A., Xie, L., Sanner, S., Cebrian, M., Yu, H., & Van Hentenryck, P. (2017). Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity. In Proceedings of the 26th International Conference on World Wide Web (pp. 735–744). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3038912.3052650

    2. Rizoiu, M.-A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018). #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate. In International AAAI Conference on Web and Social Media (ICWSM ’18) (pp. 1–10). Stanford, CA, USA. Retrieved from https://arxiv.org/abs/1802.09808

    3. Rizoiu, M.-A., Mishra, S., Kong, Q., Carman, M., & Xie, L. (2018). SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations. In Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18 (pp. 419–428). Lyon, France: ACM Press. https://doi.org/10.1145/3178876.3186108

    4. Kern, M. L., McCarthy, P. X., Chakrabarty, D., & Rizoiu, M.-A. (2019). Social media-predicted personality traits and values can help match people to their ideal jobs. Proceedings of the National Academy of Sciences, 116(52), 26459–26464. https://doi.org/10.1073/pnas.1917942116

    5. Dawson, N., Williams, M.-A., & Rizoiu, M.-A. (2021). Skill-driven recommendations for job transition pathways. PLOS ONE, 16(8), e0254722. https://doi.org/10.1371/journal.pone.0254722

    6. McCarthy, P. X., Gong, X., Eghbal, S., Falster, D. S., & Rizoiu, M.-A. (2021). Evolution of diversity and dominance of companies in online activity. PLOS ONE, 16(4), e0249993. https://doi.org/10.1371/journal.pone.0249993

    7. Kong, Q., Rizoiu, M.-A., & Xie, L. (2020). Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 645–654). New York, NY, USA: ACM. https://doi.org/10.1145/3340531.3411861

    8. Dawson, N., Molitorisz, S., Rizoiu, M.-A., & Fray, P. (2021). Layoffs, inequity and COVID-19: A longitudinal study of the journalism jobs crisis in Australia from 2012 to 2020. Journalism, 146488492199628. https://doi.org/10.1177/1464884921996286

    9. Unwin, H. J. T., Routledge, I., Flaxman, S., Rizoiu, M.-A., Lai, S., Cohen, J., … Bhatt, S. (2021). Using Hawkes Processes to model imported and local malaria cases in near-elimination settings. PLOS Computational Biology, 17(4), e1008830. https://doi.org/10.1371/journal.pcbi.1008830

    10. Rizoiu, M.-A., Wang, T., Ferraro, G., & Suominen, H. (2019). Transfer Learning for Hate Speech Detection in Social Media. In International AAAI Conference on Web and Social Media. Retrieved from http://arxiv.org/abs/1906.03829

    11. Wu, S., Rizoiu, M.-A., & Xie, L. (2018). Beyond Views: Measuring and Predicting Engagement in Online Videos. In International AAAI Conference on Web and Social Media (ICWSM ’18) (pp. 1–10). Stanford, CA, USA. Retrieved from https://arxiv.org/abs/1709.02541

  • Social Network Analysis And Network Disruption, Online Behaviour And Profiling, Social Influence – Micro, Meso And Macro Levels, Disinformation And Social Cohesion, and Radicalisation And Extremism


  • Twitter Handle     @andrei_rizoiu
    Email Address marian-andrei.rizoiu@uts.edu.au

 

Marian-Andrei Rizoiu

Overview

Dr Marian-Andrei Rizoiu is a lecturer in Computer Science at The University of Technology Sydney. He is interested in stochastic behavioural modelling of human actions online, at the intersection of applied statistics, artificial intelligence and social data science. He leads the Behavioral Data Science group, which studies human attention dynamics in the online environment, the emergence of influence and opinion polarisation. His research has made several key contributions to online popularity prediction, real-time tracking and countering disinformation campaigns, and understanding shortages and mismatches in labour markets. First, he has developed theoretical models for online information diffusion, which can account for complex social phenomena, such as the rise and fall of online popularity, the spread of misinformation, or the adoption of disruptive technologies. Second, he built a skill-based real-time occupation transition recommender system usable in periods of massive disruptions (such as COVID-19). Third, he approached questions such as “Why did X become popular, but not Y?” and “How can problematic content be detected based solely on how it spreads?” with implications in detecting the spread of conspiracy theories and disinformation campaigns. Finally, he linked social media predicted personality profiles with worker occupations, applicable in building personalising career recommendations. Marian-Andrei’s research receives funding from selective funders such as Facebook Research and Defence Science and Technology (DST).

In addition, he publishes in the most selective venues, such as the PNAS, PLOS ONE, PLOS Computations Biology, WWW, NeurIPS, IJCAI, and CIKM. As a result, his work has received significant media attention—including Bloomberg Business Week, Nature Index, BBC, and World Economic Forum. Marian-Andrei disseminates his research to the broader public by regularly contributing to The Conversation. In addition, he also leverages his research to real societal impact by, for example, serving as an expert for the NSW government’s Defamation Law Reform or providing evidence for the Australian Federal Senate inquiry into media diversity. See more at www.rizoiu.eu.