Using Natural Language Processing and Machine Learning techniques to automatically detect cyberbullying.

Bullying affects millions of children throughout the world each year. The Department for Education states that in the UK alone, 1 in 6 children aged 10-15 have reported being bullied, and 7% of these children have experienced some form of cyberbullying and cyberthreats made to them.
Automatic cyberbullying detection is a task of growing interest, particularly in the Natural Language Processing and Machine Learning communities. As a result, it can be used to prevent individuals from receiving harmful online content in social networks, therefore aiding in the reduction of the incidence of cyberbullyi ng.
This is a rapidly growing field of work, and I am excited to be contributing to it, especially since the state -of-the-art in automatic detection is developing in ways that we never imagined possible. However, one area continues to be overlooked and that is machine translation, especially in the Arabic language. This is due to the lexicology and sentiment analysis in the various dialects; MSA (modern standard Arabic) that is used for reading and writing official texts is not the same that is used forspeaking and writing in social media networks.
Using my background in both languages, I aim to deepen the understanding of how machine translation operates in Arabic, and how hate speech detection and hence cyberbullying detection and deterrence can be implemented on a wider scale. Youth from many backgrounds can benefit from the results that this research will bring.
1 am aware of the high level of knowledge and experience I will need to gain within many areas: some of which are machine learning, Python programming, deep learning, sentiment analysis, lexicology, and better research methods, and I am looking forward to rising to the challenge. I am hoping to make use of all the state-of-the-art technologies that we have available at UCL as well as collaborating with the leading innovators in this field to make an impact in this field of research.

The EPSRC Centre for Doctoral Training in Cybersecurity will train over 55 experts in multi-disciplinary aspects of cybersecurity, from engineering to crime science and public policy.

Short term impacts are associated with the research outputs of the 55+ research projects that will be undertaken as part of the doctoral studies of CDT students. Each project will tackle an important cybersecurity problem, propose and evaluate solutions, interventions and policy options. Students will publish those in international peer-reviewed journals, but also disseminate those through blog posts and material geared towards decision makers and experts in adjacent fields. Through industry placements relating to their projects, all students will have the opportunity to implement and evaluate their ideas within real-world organizations, to achieve short term impact in solving cybersecurity problems.

In the longer term graduates of the CDT will assume leading positions within industry, goverment, law enforcement, the third sector and academia to increase the capacity of the UK in being a leader in cybersecurity. From those leadership positions they will assess options and formulate effective interventions to tackle cybercrime, secure the UK's infrastructure, establish norms of cooperation between industries and government to secure IT systems, and become leading researcher and scholars further increasing the UK's capacity in cybersecurity in the years to come. The last impact is likely to be significant give that currently many higher education training programs do not have capacity to provide cybersecurity training at undergraduate or graduate levels, particularly in non-technical fields.

The full details of our plan to achieve impact can be found in the "Pathways to Impact" document.