Multi-Modal Unreliable Information Detection through the Synergy of Natural Language Processing and Complex Network Analysis

Eligibility: UK/International (including EU) graduates with the required entry requirements

Funding details: Bursary, additional allowances

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: October 25 2023

Interview date: Will be confirmed to shortlisted candidates

Start date: January 2024

For further details contact: Dr Xiaorui Jiang

Project Details

PLEASE NOTE: This is a 4-year collaborative studentship which requires the candidate to spend two full years based at Coventry University (UK) and two years based at an A*Star Research Institute (Singapore). The usual pattern is first and fourth years at Coventry and second and third year at an A*Star Research Institute.

Coventry University and A*Star will only cover the stipend up to a maximum of two years each. Changes to the mobility pattern will only be considered under exceptional circumstances and can impact on the duration of the course and level of funding available. Should a candidate request any changes to mobility which results in the period spent in either the UK or Singapore extending beyond two years then the candidate is responsible for covering the stipend for that period.

Unreliable information, e.g., conspiracy, rumour, misinformation or disinformation, may (unintentionally or intentionally) mislead and misconceptualize the public against certain entities or events. In the 2016 USA presidential election, both the Democratic and Republican Parties spread fake news to influence the election outcome. Together with closely related issues like fake news and rumours, dis/misinformation has been seen as an infodemic crisis by the WHO in parallel to the COVID-19 pandemic.

Various studies demonstrated that unreliable information should be detected from the multi-relational social networks around spreaders and the multi-relational propagation networks around information. But huge research gaps exist. Existing studies mainly work on one type of network, although patterns of interactions between various types of entities in the heterogenous social networks around information and spreaders should be a strong indicator.

The project aims to build a set of resources and tools targeting at the deep understanding and effective modelling of various types of multi-modal information sources, from a complex network point of view, towards accurate and efficient unreliable information detection. To achieve this, we will analyse information spreaders’ full-scale behavioural history of information consumption and social interaction, and utilise the patterns of information propagation dynamics and text network structure.

Funding

Tuition fees, bursary (£18,622 p/a) and additional allowances.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Candidate specification

Entry criteria for applicants to PhD

A bachelor’s (honours) degree in a relevant discipline/subject area with a minimum classification of 2:1 and a minimum mark of 60% in the project element (or equivalent), or an equivalent award from an overseas institution.
PLUS
the potential to engage in innovative research and to complete the PhD within 3.5 years
An adequate proficiency in English must be demonstrated by applicants whose first language is not English. The general requirement is a minimum overall IELTS Academic score of 7.0 with a minimum of 6.5 in each of the four sections, or the TOEFL iBT test with a minimum overall score of 95 with a minimum of 21 in each of the four sections.

For further details please visit: https://www.coventry.ac.uk/research/research-opportunities/research-students/making-an-application/research-entry-criteria/