Below is an autobiography student profile about Jeffery Ansah, a genius who holds a PhD from the University of South Australia:
I desire to land myself into a research/data science/data analyst role where I can provide cutting edge solutions to solve real-world problems.
My research topic:
Discovery and use of Twitter network structural features for civil unrest prediction
My research focuses on developing Data Mining and Machine Learning Techniques using the rich amount of social media information to predict future events. In my research, I have designed a model that tracks information propagating through a large group of online users on Twitter. Given any piece of information, the model is able to track how fast the information is travelling, the size of the community of people involved, the demographics of the various groups involved in the conversations. I combine such useful data from social media with machine learning techniques to build smart computers that will understand human behaviours and use those behavioural patterns to predict future protest events.
With the advancement of new technologies, social media platforms such as Twitter and Facebook has become a rich source of information. The statistics show that if Twitter and Facebook were physical countries, it would be the 3rd largest country in the world.
My research focuses on developing Data Mining and Machine Learning Techniques using the rich amount of social information to predict civil unrest events. In other words, my research focuses on using data from social media to build smart machines that will understand human behaviours and use those behavioural patterns to anticipate and predict future protest events. If a protest, strike action, or demonstration is going to happen, we want to know where (i.e location) it is going to happen when it going to happen (date and time), and the why (i.e the cause). We expect these smart machines we are building to tell us this information at least 3 days ahead of the event.
In my research, I have designed a model that tracks information propagating through a group of online users on Twitter. Given any piece of information, the model is able to track how fast the information is travelling, the size of the community of people involved, the demographics of the various groups involved in the conversations, the trending keywords and useful hashtag, etc. With is information we have developed a machine learning model that predict future protest events across all the states in Australia with an accuracy of 85%. This invention has been published in an A-rank Conference and Springer journal https://link-springer-com.access.library.unisa.edu.au/chapter/10.1007/978-3-319-93040-4_61.
I have also designed a framework called SensorTree that can detect events as well as precursors to events in the future. SensorTree models the communication patterns of online users as sensors. These Sensors combine Newtons Law of Motion and propagation metrics to measure the change in acceleration of conversations and community growth for event detection. We tested this framework in Australia and Indonesia. The framework performs better than the existing state of the art models. SensorTree framework is effective for event detection without any language restriction. This paper was published in Web Information Systems Engineering (WISE 2018, A-ranked), one of the prestigious DataMining and Web Science conferences in Dubai last year.
Awards, Grants & Activities
- Applied Research Grant to attend the international conference on Web Information Systems, Zayed University, Dubai, UAE, November 2018.
- Applied Research Grant to attend the Pacific Asia Conference on Knowledge Discovery in Databases (PAKDD)
- State Mentor SA, Australian Maths Trust, digIT Programme 2019
- President of the School of Information Technology and Mathematical Sciences Higher Degree Research Club, UniSA, 2016, 2017.
- Mentor for the UniSA Mentor Program, 2018
- Accepted into the UniSA Premium Leadership Program, 2018
- Finalist and third place in the UniSA Three Minute Thesis, 2018
- Winner of the UniSA ITMS People’s Choice Award, 2018
Source: Jeffery Ansah|DataToDecisionCRC
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