Datasets:

MidhunKanadan
/

populist-argument-schemes

@@ -1,12 +1,11 @@
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 license: mit
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 # Populist Argument Schemes 🗣️
 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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 ## Dataset Summary
 This dataset contains **English-translated tweets** from:
@@ -17,16 +16,12 @@ This dataset contains **English-translated tweets** from:
 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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 ## Features
 - `Politician`: Name of the political figure (e.g., "Trump")
 - `Argument`: The tweet text (translated into English)
 - `Argument Scheme`: The full name of the primary argumentation scheme (e.g., "Argument from Consequences")
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 ## Argument Scheme Labels
 The dataset includes 13 argument schemes:
@@ -47,18 +42,14 @@ The dataset includes 13 argument schemes:
 | PR    | Argument from Practical Reasoning      |
 | VV    | Victimization                          |
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 ## Use Cases
 This dataset can be used for:
 - Fine-tuning LLMs for **argument scheme classification**
 - Training models for **argument mining** and **fallacy detection**
 - Studying **populist rhetoric** and comparative discourse analysis
 - Building educational tools for **teaching argumentation theory**
-```markdown
 ## How to Use 🧠
 You can easily load this dataset using the 🤗 `datasets` library, which allows seamless integration with Hugging Face Transformers, evaluation tools, and fine-tuning pipelines.
@@ -89,9 +80,4 @@ print(dataset["train"][0])
 If you use this dataset, please cite the original author:
-> Macagno, F. (2022). Argumentation schemes, fallacies, and evidence in politicians’ argumentative tweets – a coded dataset. *Data in Brief*, 44, 108501. https://doi.org/10.1016/j.dib.2022.108501
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 ---
 license: mit
 ---
 # Populist Argument Schemes 🗣️
 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.
 ## Dataset Summary
 This dataset contains **English-translated tweets** from:
 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.
 ## Features
 - `Politician`: Name of the political figure (e.g., "Trump")
 - `Argument`: The tweet text (translated into English)
 - `Argument Scheme`: The full name of the primary argumentation scheme (e.g., "Argument from Consequences")
 ## Argument Scheme Labels
 The dataset includes 13 argument schemes:
 | PR    | Argument from Practical Reasoning      |
 | VV    | Victimization                          |
 ## Use Cases
 This dataset can be used for:
 - Fine-tuning LLMs for **argument scheme classification**
 - Training models for **argument mining** and **fallacy detection**
 - Studying **populist rhetoric** and comparative discourse analysis
 - Building educational tools for **teaching argumentation theory**
 ## How to Use 🧠
 You can easily load this dataset using the 🤗 `datasets` library, which allows seamless integration with Hugging Face Transformers, evaluation tools, and fine-tuning pipelines.
 If you use this dataset, please cite the original author:
+> Macagno, F. (2022). Argumentation schemes, fallacies, and evidence in politicians' argumentative tweets – a coded dataset. *Data in Brief*, 44, 108501. https://doi.org/10.1016/j.dib.2022.108501