# BAN-PL: a Novel Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl web service

arXiv: https://arxiv.org/abs/2308.10592 · HTML: https://mlsanity.com/paper/2308.10592/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 85 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [All You Need is "Love": Evading Hate-speech Detection](https://mlsanity.com/paper/1808.09115/) (2018)
- [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://mlsanity.com/paper/2005.00630/) (2020)
- [Pre-training Polish Transformer-based Language Models at Scale](https://mlsanity.com/paper/2006.04229/) (2020)
- [The Origin and Value of Disagreement Among Data Labelers: A Case Study of the Individual Difference in Hate Speech Annotation](https://mlsanity.com/paper/2112.04030/) (2021)
- [APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets](https://mlsanity.com/paper/2202.12459/) (2022)
- [Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity](https://mlsanity.com/paper/1910.11922/) (2019)

## Built on by (most cited first, 6 of 6)

- [PL-Guard: Benchmarking Language Model Safety for Polish](https://mlsanity.com/paper/2506.16322/) (2025)
- [POLygraph: Polish Fake News Dataset](https://mlsanity.com/paper/2407.01393/) (2024)
- [The PLLuM Instruction Corpus](https://mlsanity.com/paper/2511.17161/) (2025)
- [Long-Context Encoder Models for Polish Language Understanding](https://mlsanity.com/paper/2603.12191/) (2026)
- [Polish ModernBERT: The Long and Short of Polish Language Understanding](https://mlsanity.com/paper/2609.01379/) (2026)
- [Baszta: Data-Centric Fine-Tuning of a Polish Multi-Label Safety Classifier](https://mlsanity.com/paper/2609.29266/) (2026)

## Abstract (arXiv)

Since the Internet is flooded with hate, it is one of the main tasks for NLP experts to master automated online content moderation. However, advancements in this field require improved access to publicly available accurate and non-synthetic datasets of social media content. For the Polish language, such resources are very limited. In this paper, we address this gap by presenting a new open dataset of offensive social media content for the Polish language. The dataset comprises content from Wykop.pl, a popular online service often referred to as the "Polish Reddit", reported by users and banned in the internal moderation process. It contains a total of 691,662 posts and comments, evenly divided into two categories: "harmful" and "neutral" ("non-harmful"). The anonymized subset of the BAN-PL dataset consisting on 24,000 pieces (12,000 for each class), along with preprocessing scripts have been made publicly available. Furthermore the paper offers valuable insights into real-life content moderation processes and delves into an analysis of linguistic features and content characteristics of the dataset. Moreover, a comprehensive anonymization procedure has been meticulously described and applied. The prevalent biases encountered in similar datasets, including post-moderation and pre-selection biases, are also discussed.