Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new $\textit{Us vs. Them}$ dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.
Code (1)
Tasks
Multi-Task LearningPopulist attitudeSimilar Papers 제목 키워드 기반
An Impossible Dialogue! Nominal Utterances and Populist Rhetoric in an Italian Twitter Corpus of Hate Speech against Immigrants
The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utte…
ArticlesMedia Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media
News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vacc…
Causal InferenceComparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation
News media typically present biased accounts of news stories, and different publications present different angles on the same event. In this research, we investigate how different publications differ in their approach to…
ArticlesSentiment AnalysisDebiasing International Attitudes: LLM Agents for Simulating US-China Perception Changes
Large Language Models (LLMs) offer transformative opportunities to address the longstanding challenge of modeling opinion evolution in computational social science. This study investigates how media influences cross-bord…
Automatically Neutralizing Subjective Bias in Text
Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiqui…
DecoderSentenceText Generation