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README.md
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license: mit
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license: mit
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---
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# Populist Argument Schemes 🗣️
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A curated dataset of translated tweets from four political leaders, annotated with argument schemes according to argumentation theory. The dataset enables fine-tuning and evaluation of language models for argument mining, rhetorical analysis, and populist discourse detection.
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---
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## Dataset Summary
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This dataset contains **English-translated tweets** from:
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- **Matteo Salvini** 🇮🇹
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- **Jair Bolsonaro** 🇧🇷
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- **Donald Trump** 🇺🇸
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- **Joe Biden** 🇺🇸
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Each tweet is annotated with its **primary argumentation scheme** (based on *Argument 1*) following a scheme taxonomy inspired by Walton and Macagno. The dataset is derived from Fabrizio Macagno's original annotated corpus on the *Language of Populism* and translated for cross-lingual NLP applications.
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---
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## Features
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- `Politician`: Name of the political figure (e.g., "Trump")
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- `Argument`: The tweet text (translated into English)
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- `Argument Scheme`: The full name of the primary argumentation scheme (e.g., "Argument from Consequences")
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---
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## Argument Scheme Labels
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The dataset includes 13 argument schemes:
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| Code | Argument Scheme |
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|-------|----------------------------------------|
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| AA | Argument from Analogy |
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| AC | Argument from Consequences |
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| AH | Ad Hominem |
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| AS | Argument from Sign |
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| AV | Argument from Values |
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| BEX | Argument from Best Explanation |
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| CE | Argument from Cause to Effect |
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| CLASS | Argument from Classification |
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| CO | Argument from Commitment |
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| PK | Argument from Position to Know |
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| PO | Argument from Popular Opinion |
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| PR | Argument from Practical Reasoning |
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| VV | Victimization |
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---
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## Use Cases
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This dataset can be used for:
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- Fine-tuning LLMs for **argument scheme classification**
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- Training models for **argument mining** and **fallacy detection**
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- Studying **populist rhetoric** and comparative discourse analysis
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- Building educational tools for **teaching argumentation theory**
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