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Generalizing Political Leaning Inference to Multi-Party Systems: Insights from the UK Political Landscape (2312.01738v1)

Published 4 Dec 2023 in cs.SI and cs.CY

Abstract: An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has been a body of research attempting to infer the political leaning of social media users, this has been typically simplified as a binary classification problem (e.g. left vs right) and has been limited to a single location, leading to a dearth of investigation into more complex, multiclass classification and its generalizability to different locations, particularly those with multi-party systems. Our work performs the first such effort by studying political leaning inference in three of the UK's nations (Scotland, Wales and Northern Ireland), each of which has a different political landscape composed of multiple parties. To do so, we collect and release a dataset comprising users labelled by their political leaning as well as interactions with one another. We investigate the ability to predict the political leaning of users by leveraging these interactions in challenging scenarios such as few-shot learning, where training data is scarce, as well as assessing the applicability to users with different levels of political engagement. We show that interactions in the form of retweets between users can be a very powerful feature to enable political leaning inference, leading to consistent and robust results across different regions with multi-party systems. However, we also see that there is room for improvement in predicting the political leaning of users who are less engaged in politics.

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Authors (3)
  1. Joseba Fernandez de Landa (5 papers)
  2. Arkaitz Zubiaga (88 papers)
  3. Rodrigo Agerri (41 papers)
Citations (2)
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