Papers by David Abadi

2 papers
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions (2021.eacl-main)

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Challenge: Populist rhetoric has risen across the political sphere in recent years, but computational approaches to it have been scarce.
Approach: They propose a dataset of 6861 reddit comments annotated for populist attitudes and a set of multi-task learning models that leverage emotion and group identification as auxiliary tasks.
Outcome: The proposed models leverage emotion and group identification as auxiliary tasks to model populist rhetoric tasks.
The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse (2020.findings-emnlp)

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Challenge: Existing computational models of political discourse do not incorporate metaphor and emotion in their functions.
Approach: They propose to combine metaphor, emotion and political rhetoric to model political discourse . they show that they advance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.
Outcome: The proposed models improve political discourse prediction, party affiliation and framing of policy issues.

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