Inferring variation in the meaning of political terms over time
COLLABORATORS: Dr Chico Camargo, CEMPS, University of Exeter, affiliated academic at IDSAI, Co-I: Dr Barbara McGillivray, Turing Research Fellow, The Alan Turing Institute, Senior Research
IDSAI Research Fellow: Dr Bertrand Nortier
IDSAI Research Fellow: Dr Bertrand Nortier
Description: Language is an important force shaping politics and guiding collective sensemaking. This is evident with the rise of terms such as "gender pay gap", "Brexit" even "lockdown". In this context, lexical semantic change, i.e. the change in meaning of words, is fast, wide, and weaponised. This makes it pressing to ask: can we detect and measure how meanings change or persist in certain communities? Can we track lexical semantic change at scale, in real time?
This project will develop new data science methods to track the semantic change of political terms. It will combine tools from natural language processing, machine learning, and network science, to make "socially aware" computational models of lexical semantic change. We will do so by extending methods of semantic change detection based on word embeddings, as well as token and sense embeddings, applying them to a series of political terms present in debates in the UK Parliament over the last decade, and incorporating metadata on party affiliation, debate topic, and debate participants, to reveal how word meanings were shared – and built – across different parties and debates. As such, this project will enable us to measure the dynamics of semantic divergence and convergence, as well as coordinated behaviour in the UK Parliament.
This project will develop new data science methods to track the semantic change of political terms. It will combine tools from natural language processing, machine learning, and network science, to make "socially aware" computational models of lexical semantic change. We will do so by extending methods of semantic change detection based on word embeddings, as well as token and sense embeddings, applying them to a series of political terms present in debates in the UK Parliament over the last decade, and incorporating metadata on party affiliation, debate topic, and debate participants, to reveal how word meanings were shared – and built – across different parties and debates. As such, this project will enable us to measure the dynamics of semantic divergence and convergence, as well as coordinated behaviour in the UK Parliament.
