School of Social and Political Science

Description

The Neuropolitics Research Lab produces transdisciplinary research, utilizing developments in the cognitive neurosciences, to shed new light on political attitudes, identities and decision-behaviours. As part of this effort, we are a launching a series of 'NRLabs Talks' to highlight and discuss some of the exciting research taking place in this area. There will also be an opportunity to join the NRLabs team for coffee and a light lunch after the talk.

For our next event, we will be joined by Dr Clare Llewellyn, who will kindly be presenting her research findings from the project ‘Enriching Exhibitions Scholarship: Reconciling Knowledge Graphs and Social Media from Newspaper Articles to Twitter’. This innovative project applied advanced computational techniques, such as text mining and machine learning, to enrich linked data records with information relating to museum exhibitions and map connections between collections around the world to make cultural knowledge more accessible to scholars, artists and the public. The project was generously funded by the AHRC/NEH New Directions for Digital Scholarship in Cultural Institutions programme, supporting US-UK collaboration, involving partners from the Universities of Edinburgh, Yale and Oxford, including the Ashmolean Museum.

Dr Llewellyn is interested in the development and definition of cross-disciplinary methodological and ethical techniques and standards in the GovTech domain. She has extensive experience in working in a collaborative trans-disciplinary environment and has produced co-authored papers with academics from informatics, politics, social work, history, botany, criminology, linguistics, information studies and library science. More recently, she has specialised in collaborating with social scientists, where she been embedded in a trans-disciplinary lab, expanding and exploited her unique combination of skills.

With her research, she has developed novel data analysis techniques, underpinned by data science technologies such as natural language processing, supervised and unsupervised machine learning and statistical analysis. Her recent focus has been in developing and implementing studies to gather and analyse social media data and in using these results either in combination with, or as an underpinning for, experimental and quantitative social science research. Her work in this area is unique, utilising a longitudinal multiple aspect collection approach to gathering social media data. This involves gathering several parallel datasets over extended time periods allowing the observation of changes, the ability to map to changes in data to changes in public opinion and enables analysis from multiple viewpoints.