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Identifying Populist Paragraphs in Text: A machine-learning approach

arXiv 2106.03161 ↗First posted 6 Jun 2021cs.CLcited by 0

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Abstract (arXiv)

Abstract: In this paper we present an approach to develop a text-classification model which would be able to identify populist content in text. The developed BERT-based model is largely successful in identifying populist content in text and produces only a negligible amount of False Negatives, which makes it well-suited as a content analysis automation tool, which shortlists potentially relevant content for human validation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 43 references that are in the map. First posted on arXiv 6 Jun 2021.

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4 of 43 references.

References · 4

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 112
  4. 2019Large-Scale Multi-Label Text Classification on EU Legislationcited by 104

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