I am a Lecturer in Computer Science in the School of Mathematics and Computer Science, Faculty of Science and Engineering (FSE) at the University of Wolverhampton, UK. Before joining FSE, I was a Postdoctoral Research Fellow in Natural Language Processing (NLP) at the Research Group in Computational Linguistics (RGCL), University of Wolverhampton, UK.
I am a Fellow of the Higher Education Academy, UK. I completed the Postgraduate Certificate in Higher Education and Professional Practice (PG Cert) from the University of Wolverhampton, UK. I received PhD on "On Detection of Cyberbullying in Social Networks" from the School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia. I received Master of Computer Applications (MCA) from the ISIT Bangalore, ICFAI University, and Bachelor of Computer Applications (BCA) from the MCRPVV Bhopal. I have also done online training for Certified Information Systems Security Professional (CISSP®) from the International Information Systems Security Certification Consortium (ISC)² Ltd, USA.
University of Wolverhampton, UK
The University of Queensland, Australia
My research interest includes machine learning, information retrieval, data mining, social media - analysis and prediction, applications of natural language processing, sentiment analysis, sensitive event detection and cyberbullying detection.
I will welcome research students in the areas of natural language processing, social computing, cyber security and social media analysis. Some of the potential PhD topics are as follows:
Multilingual Cyberbullying detection in social networks:
Cyberbullying is one of the most serious social menaces in social networks affecting millions of people worldwide - particularly teenagers - in their early explorations in cyberspace. Due to the dynamic nature of streaming data and information overloading, traditional computing technologies are lagging behind the urgent need for real-time cyberbullying detection in social networks. We would welcome PhD research that aims to exploit NLP technologies to provide better linguistic understanding of individuals, and to build language models of suspected individuals' (victims, bullies and bystanders) in social networks.
Author profiling in social networks:
People often leave anonymous reviews, blogs, or messages giving their opinion on a particular product, movie, or political campaign. For many organisations, it is useful to determine the age and gender of people who like or dislike their products for marketing purposes. Similarly, in the field of forensic crime investigation, knowing the profile information (age and gender) of the author of threatening or anonymous messages, for example, is extremely important. However, this information is usually unavailable in the public domain due to privacy reasons. PhD proposals are invited that aim to develop users' age and gender prediction models in social networks by using NLP applications and techniques to explore linguistic knowledge.
Sentiment analysis in social networks:
Sentiment analysis is a process of identifying opinion from a given opinionated document into positive, negative, and neutral categories. Beyond identifying these categories, more fine-grained emotions (e.g. anger, joy, sadness, surprise, disgust and fear) are required for context reasoning and decision-making. We invite PhD proposals that investigate NLP methods, and combine them with temporal analysis, and topic analysis to capture dynamics of user behaviour and emotions with precision.
Sarcasm detection in Twitter:
Sarcasm is a way of expressing a - normally - negative, harsh or bitter message in a positive way. Sarcasm detection poses a major challenge in linguistic feature extraction and selection in short-text messages, such as determining whether the phrase "that's great, isn't!! is meant sincerely or sarcastically. PhD proposals are invited to develop user behavioural patterns and models using different NLP techniques to capture sarcastic remarks in Twitter.
Multilingual Cyber terrorism detection:
Radicalisation often leads to violent acts, such as cyber terrorism, which have a hugely negative impact on society. It is challenging to spot suspected activities relating to cyberterrorism quickly enough to act. There is scope to investigate how natural language processing, machine learning, social graph mining and visualisation can quickly locate suspected contents or traces relating to terrorist activities and trace the location of suspected individuals or groups, and we would welcome PhD proposals focusing on this area.
Note that there is no specific funding attached to any of the suggested topics; however, student having scholarship or self-funded students with strong academic background, programming skills and motivation are eligible to apply. For more information, do not hesitate to contact me.
News featured: A bright #Queensland mind working for a safer online community:
Postgraduate research showcased- A statement of the 2012 UQ Engineering Postgraduate Research Conference Chair: http://www.uq.edu.au/news/?article=24897