# The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English

arXiv: https://arxiv.org/abs/1906.02045 · HTML: https://mlsanity.com/paper/1906.02045/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection](https://mlsanity.com/paper/2401.06526/) (2024)
- [HateModerate: Testing Hate Speech Detectors against Content Moderation Policies](https://mlsanity.com/paper/2307.12418/) (2023)
- [PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework](https://mlsanity.com/paper/2306.08804/) (2023)
- [LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification](https://mlsanity.com/paper/2411.19638/) (2024)
- [Retweet communities reveal the main sources of hate speech](https://mlsanity.com/paper/2105.14898/) (2021)
- [Online Hate: Behavioural Dynamics and Relationship with Misinformation](https://mlsanity.com/paper/2105.14005/) (2021)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages](https://mlsanity.com/paper/2510.11167/) (2025)

## Abstract (arXiv)

In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK which cover two topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD). The main advantages of these datasets compared to the existing ones are identical sampling procedures, producing comparable data across languages and an annotation schema that takes into account six types of SUD and five targets at which SUD is directed. We describe the sampling and annotation procedures, and analyze the annotation distributions and inter-annotator agreements. We consider this dataset to be an important milestone in understanding and combating SUD for both languages.