Research Fellow​/Associate - PRISM

We are seeking to appoint a Research Associate to support the Political Rhetoric and Influence on Social Media (PRISM) project. This interdisciplinary APEX-funded project will investigate whether successful online political influencers in the US and UK use a distinctive rhetorical style or “brand” to engage audiences and communicate during political events.

• Supporting the development of PRISM’s interdisciplinary framework for identifying the rhetorical brand of online political influencers across US and UK election contexts.
• Collecting, cleaning, documenting and securely managing social media datasets and metadata from the 2020 and 2024 US Presidential elections and the 2024 UK General Election.
• Designing, implementing and evaluating NLP and machine-learning approaches to classify communicative frames, linguistic features and influencer styles at scale.
• Contributing to annotation, validation, survey experiment analysis, reproducible code, academic outputs and stakeholder-facing materials with the wider project team.

This is an exciting opportunity to contribute to conceptual, theoretical and methodological innovation in political communication and natural language processing, working with Professor Gibson, Dr Batista-Navarro and the wider PRISM team.

We are looking for a collaborative researcher with advanced computational skills and relevant social science expertise. You should demonstrate:

• A PhD, or close to completion, in Political Science, Media and Communication, Sociology or a related social science field.
• Strong programming and computational methods expertise, including Python, R or similar languages, machine learning, NLP, text mining and reproducible coding practices.
• Experience analysing social media data using qualitative & quantitative methods, with excellent academic writing, collaboration, organisation and communication skills.

• Experience with transformer-based NLP models, web or social media engagement, policy briefing, research server environments, data visualisation or academic subjects such as social statistics.

This role would suit a forward-thinking individual who is passionate about delivering outstanding technical support and enabling excellence in practical teaching.

• Generous employer contribution pension
• 29 days annual leave plus bank holidays, and Christmas closure
• Ride to work and EV car scheme available

For more information, please see University of Manchester Benefits. You can also find information on our Flexible and Hybrid working here.