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The dynamics of human attention in the online environment

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

This project will develop and evaluate stochastic models for the spread of information over complex social networks, in which the individual actions -- such as retweets, posts or shares -- are only partially observed. This can arise when only the actions of particular individuals are observed (say, when following some individuals on Twitter), or when only counts of actions are recorded (as in the case of Youtube attention series).

Stochastic point process modelling is the class of models behind several state of the art work for modelling diffusion in online social systems [1-4]. However such models typically require observing every online action, and they make restrictive modelling assumption. This project leverages recent advances in linking micro-modelling (individual actions) to macro-modelling (interval-censored observations) [5], and it aims to develop tools and methods to model diffusion of information across platform boundaries (say, from Reddit to Twitter and Youtube) and at different granularities. The privileged avenue are multi-variate point processes, in which some dimensions are observed as interval-censored.

The PhD student will be located within the newly formed DataSci Research Centre at UTS, in the Behavioral Data Science group. DataSci counts 15 members, with research interests spanning across smart logistics, transportation, and human dynamics. The Research Centre has both strong ties with industry, as well as world-class research, providing the ideal environment for solving real-world originating issues, in close proximity to both academia and industry.

The candidate
Interested candidates must have solid background knowledge in statistics, machine learning, and strong programming capabilities. Experience with big social data, extracting and handling web- and social media-originating data is a big plus. We are looking for a candidate with a master by research qualification, and demonstrated research capabilities (preferably through publications). Candidates with publications in major conferences/journals will be prioritised. The position will be open until the ideal candidate is identified.

This project will advance both the theoretical tools, as well as apply it to real problems in sociology and political science, through inter-disciplinary collaborations.

[1] Zhao, Q., Erdogdu, M. A., He, H. Y., Rajaraman, A., & Leskovec, J. (2015). SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
[2] Mei, H., & Eisner, J. (2017). The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process. In Advances in Neural Information Processing Systems 30 (pp. 6757–6767).
[3] Zarezade, A., De, A., Rabiee, H., & Rodriguez, M. G. (2017). Cheshire: An Online Algorithm for Activity Maximization in Social Networks.
[4] Farajtabar, M., Du, N., Rodriguez, M. G., Valera, I., Zha, H., & Song, L. (2014). Shaping Social Activity by Incentivizing Users. In Advances in Neural Information Processing Systems (Vol. 27, p. 5365).
[5] 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.

About the Faculty

The Faculty of Engineering and Information Technology at UTS is a world-class faculty with a growing reputation for its quality and impact. Our research is highly advanced, industry-focused and part of the lively and rigorous research culture at UTS.

Focused on ’practical innovation’, our researchers are pioneering research solutions with real-world impact. They’re recognised leaders in their fields, responsible for delivering new, better and more cost-effective innovative solutions to current national and international challenges.

About the University

UTS is a dynamic and innovative university in central Sydney. One of Australia’s leading universities of technology, UTS has a distinct model of learning, strong research performance and a leading reputation for engagement with industry and the professions.

UTS has a culturally diverse campus life and vibrant international exchange study and research programs that prepare graduates for the workplaces of today and the future.

Our world-leading research centres span a range of disciplines, including physical, biological and engineering sciences, and contemporary fields such as design, nanotechnology and sustainability. Our researchers provide practical and relevant solutions to issues of national and international importance and equip graduates with the latest discipline-specific skills and practices.

Funding Notes

Scholarships available
Scholarships are tax-free and valued at $27,082 pa (tax-exempt) for 3 yrs plus a 1 x possible 6 month extension. Additionally, fee-waiver scholarships are available for highly qualified international candidates.

Closing date for next intake
Australian Domestic students: 30th April 2019 (for commencement July 2019) or 30 September 2019 (for commencement January 2020)
International Candidates: 30 June 2019 (for commencement January 2020)

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