A Digitally Resilient Framework for Hate Speech Detection

This project investigates the detection of hate speech in combined multimodal and multilingual settings. This will be achieved by developing state-of-the-art AI algorithms which combine textual, auditory, and visual modalities.

Studentship group name

Digital Resilience

Department/School

School of Computer Science and Electrical Engineering                                                                                                                  

Project Description

This project deals with the application of deep learning in natural language processing and computer vision to devise resilient approaches which allow the automatic detection of hate speech in multiple languages. Hateful comments and posts on social media platforms have become an important issue in recent times. Such hate speech can be directed against people on the basis of their race, ethnicity, religion, sex, sexual orientation, disability or even their political stance. Hate speech can occur in various languages through text, audio, and visual modalities, making its automatic detection challenging for artificial intelligence (AI) algorithms. 

The main issue addressed in this project is the improved performance of multimodal hate speech detection for multiple languages. Current literature focuses on combating hate speech online and tries to tackle it using the textual or written modality. Recent efforts to combat multilingual hate speech are limited in terms of approaches that are linguistically sound. In the current atmosphere, analysis of hate speech does not just depend on textual modality but also needs to take into account the audio and visual modalities. The objectives of this project are to (1) design and deploy an open-source framework which allows resilient detection of hate speech for multiple modality combinations, (2) train and evaluate AI models which are a part of the framework discussed, and (3) improve upon the existing multimodal approaches for hate speech detection.

The PhD would be based at the Surrey Centre for Cyber Security (SCCS -- https://www.surrey.ac.uk/surrey-centre-cyber-security) and Surrey Institute for People-Centred AI (https://www.surrey.ac.uk/artificial-intelligence)  

Candidate Profile

Candidates should ideally have a background or a strong interest in one or more of the following areas: Natural Language Processing, Computer Vision, Machine Learning, Deep Learning, Artificial Intelligence, and Social Media Domain.

How to apply

Open to UK and International students starting in October 2023.

Applications should be submitted via the Computer Science PhD programme page. In place of a research proposal you should upload a document stating the title of the projects (up to 2) that you wish to apply for and the name(s) of the relevant supervisor. You must upload your full CV and any transcripts of previous academic qualifications. You should enter ’Faculty Funded Competition’ under funding type.

Funding

The studentship will provide a stipend at UKRI rates (currently £17,668 for 2022/23) and tuition fees for 3.5 years. An additional bursary of £1700 per annum for the duration of the studentship will be offered to exceptional candidates.