Fact vs. Opinion: the Role of Argumentation Features in News Classification
| Autor: | Daniel Preotiuc-Pietro, Tariq Alhindi, Smaranda Muresan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Argumentative
Computer science Event (computing) 0202 electrical engineering electronic engineering information engineering Public trust 020201 artificial intelligence & image processing 02 engineering and technology Representation (arts) 010501 environmental sciences 01 natural sciences Linguistics 0105 earth and related environmental sciences Argumentation theory |
| Zdroj: | COLING |
| Popis: | A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual information) and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. Our hypothesis is thatthe nature of argumentative discourse is important in distinguishing between news stories andopinion articles. We show that argumentation features outperform linguistic features used previ-ously and improve on fine-tuned transformer-based models when tested on data from publishersunseen in training. Automatically flagging opinion pieces vs. news stories can aid applicationssuch as fact-checking or event extraction. |
| Databáze: | OpenAIRE |
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