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Learning from Machines: Differentiating US Presidential Campaigns with Attribution and Annotation
Identifying the differing ways in which political actors and groups express themselves is a key task in the study of legislatures, campaigning, and communication. A variety of computational tools exist to help find and describe these patterns, typically summarizing differences with weighted word lists representing either lexical frequencies or semantic fields. I identify two limits to the inferences that can be made based on this method: the ambiguity of the semantic value of words without wider context and an inability to detect differences outside of lexical semantics. I present a combination of text annotation and deep-learning feature attribution, a set of techniques for evaluating the relative importance of data inputs to the prediction of a neural network classifier, as an alternative means of identifying differentiating language usage in political texts. Results are evaluated with comparison to existing text-as-data tools on a dataset of US presidential campaign advertisements from Facebook between 2017 and 2020.
Date:
19 May 2022, 13:00
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Skills Lab (2nd floor) - and on Zoom
Speaker:
Musashi Jacobs-Harukawa (Oxford)
Organisers:
Musashi Harukawa (University of Oxford),
Klaudia Wegschaider (University of Oxford),
Marta Antonetti (University of Oxford),
Nelson Ruiz (University of Oxford)
Host:
Nelson Ruiz (University of Oxford)
Part of:
Politics Research in Progress Seminar Series
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Klaudia Wegschaider
