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Dr. Zainab Zaidi

Lecturer, Electrical Engineering

Contact

Campus

Berwick Campus

Biography

Dr Zainab Zaidi is a lecturer in Engineering within the Institute of Innovation, Science and Sustainability (IISS). Her research explores the impact of misinformation and disinformation on public opinion expressed over social media. In this research, social media posts are analysed about contentious topics, such as the COVID-19 vaccine, the Russia-Ukraine war and Israel-Palestine conflict using Machine Learning (ML) and Natural Language Processing (NLP) based techniques.  

In a recent publication, Zainab analysed year-long discourse about COVID-19 vaccines on Twitter (now X) and found that the anti-vax sentiment was propagated by a large cohort of ambivalent users who were posting both in favour and against the vaccine instead of the widely popular belief that this is the work of a small but vocal hardline anti-vaxxers. 

Previously, Zainab worked as a research fellow at The University of Melbourne (2023–24) and as a researcher in the Networked Systems group in NICTA (now Data61), Sydney (2006–2011). She was also a visiting senior research fellow in King's College London, UK, in 2014. Her research at that time included the use of ML tools to improve wireless networking technologies and energy efficiency of mobile network architectures.

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More about Zainab

Qualifications

  • Bachelor of Engineering, NED University (Pakistan) 
  • Master of Science, George Mason University (USA)
  • PhD, George Mason University (USA)

Areas of interest

  • Public opinion dynamics 
  • Social networks 
  • Misinformation and disinformation 
  • Wireless networking technologies

  • Engineering basics 
  • Telecommunications and wireless networks 
  • Data science and probability 

Centre for Smart Analytics (CSA)

Zainab is part of the Centre for Smart Analytics (CSA), which aims to develop new knowledge and innovative solutions to develop smart and resilient cities, regions and industries.
  • Publications

Algorithmically-designed reward shaping for multiagent reinforcement learning in navigation

LIN: Latent Influence Network for Discovering Hidden Directed Influence Links on Social Media

Using Causality to Infer Coordinated Attacks in Social Media