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Natural Language Processing for Public Health Surveillance

The sale of sugary products is still strong, despite the recommendations to reduce their consumption. When children consume too much sugar, they can suffer from obesity, type 2 diabetes and cardiovascular disease. This is why health organisations recommend parents reduce their children's consumption of sugary products. One barrier to successfully reducing the sale of unhealthy products like chocolates & sugary drinks is aggressive marketing by companies producing them through ads on social media platforms such as Facebook and Instagram. Ads show people happiness, laughter and pleasure associated with their products and people feel compelled to provide such items at their events and this drives sales in a thriving industry. Social media has emerged as a distinctive marketing avenue for such an unhealthy industry. There is an urgent need to evaluate and address unhealthy marketing on social media to improve public health and nutrition.

Social media has become an important platform for health professionals to promote healthy living. However, the presence of unhealthy foods and food advertising on social media are causing concern among health professionals. This study will examine strategies and approaches of both industries that promote unhealthy choices and healthy diet promotion by health professionals on social media to identify knowledge gaps.

We shall employ deep learning and data mining technologies to examine the role of social media in the commercial promotion of unhealthy products through a public health lens. The PhD candidate will novel deep learning-based algorithms to extract knowledge from the text.

Our previous publications in this space can be read here

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)


Matloob Khushi - Matloob has over 2 decades of industry and academic experience. He completed his PhD in AI and Data Science from the University of Sydney, Australia in which he developed novel algorithms for understanding big genomic data. Dr. Khushi has earned various awards for his research achievements and has authored more than 70 research papers. During his postdoc (2014-2017) at the Children’s Medical Research Institute, Australia, he developed automated AI-based algorithms for expediting the drug discovery process. He has also developed solutions for the financial industry such as portfolio management, prediction of stock, forex and commodities markets. Dr. Khushi has supervised more than 100 research theses in various domains of AI and data mining and is always willing to supervise very talented PhD candidates.

Related Research Group(s)

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.