# AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI

arXiv: https://arxiv.org/abs/2501.06265 · HTML: https://mlsanity.com/paper/2501.06265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 14 of 14)

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- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [Sampled Datasets Risk Substantial Bias in the Identification of Political Polarization on Social Media](https://mlsanity.com/paper/2406.19867/) (2024)
- [Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans](https://mlsanity.com/paper/2410.13400/) (2024)

## Abstract (arXiv)

Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical strategies and ideological contexts. In this paper, we present AgoraSpeech, a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023. The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection. A two-step annotation was employed, starting with ChatGPT-generated annotations and followed by exhaustive human-in-the-loop validation. The dataset was initially used in a case study to provide insights during the pre-election period. However, it has general applicability by serving as a rich source of information for political and social scientists, journalists, or data scientists, while it can be used for benchmarking and fine-tuning NLP and large language models (LLMs).