# SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

arXiv: https://arxiv.org/abs/2105.06456 · HTML: https://mlsanity.com/paper/2105.06456/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 13 May 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [The birth of Romanian BERT](https://mlsanity.com/paper/2009.08712/) (2020)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://mlsanity.com/paper/1901.06437/) (2019)
- [MOROCO: The Moldavian and Romanian Dialectal Corpus](https://mlsanity.com/paper/1901.06543/) (2019)
- [Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features](https://mlsanity.com/paper/1709.01189/) (2017)
- [Adversarial Training for Satire Detection: Controlling for Confounding Variables](https://mlsanity.com/paper/1902.11145/) (2019)
- [A Multi-Modal Method for Satire Detection using Textual and Visual Cues](https://mlsanity.com/paper/2010.06671/) (2020)
- [FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection](https://mlsanity.com/paper/2104.04828/) (2021)
- [Fake or Real? A Study of Arabic Satirical Fake News](https://mlsanity.com/paper/2011.00452/) (2020)
- [Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation](https://mlsanity.com/paper/2007.02164/) (2020)

## Built on by (most cited first, 12 of 12)

- [Distilling the Knowledge of Romanian BERTs Using Multiple Teachers](https://mlsanity.com/paper/2112.12650/) (2021)
- [YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models](https://mlsanity.com/paper/2409.13592/) (2024)
- [RoQLlama: A Lightweight Romanian Adapted Language Model](https://mlsanity.com/paper/2410.04269/) (2024)
- [Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis](https://mlsanity.com/paper/2306.07845/) (2023)
- [A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles](https://mlsanity.com/paper/2310.06540/) (2023)
- [RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation](https://mlsanity.com/paper/2412.11317/) (2024)
- [Self Iterative Label Refinement via Robust Unlabeled Learning](https://mlsanity.com/paper/2502.12565/) (2025)
- [Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English](https://mlsanity.com/paper/2411.10730/) (2024)
- [SaRoHead: Detecting Satire in a Multi-Domain Romanian News Headline Dataset](https://mlsanity.com/paper/2504.07612/) (2025)
- [MuSaRoNews: A Multidomain, Multimodal Satire Dataset from Romanian News Articles](https://mlsanity.com/paper/2504.07826/) (2025)
- [SeLeRoSa: Sentence-Level Romanian Satire Detection Dataset](https://mlsanity.com/paper/2509.00893/) (2025)
- [Every Character Counts: From Vulnerability to Defense in Phishing Detection](https://mlsanity.com/paper/2509.20589/) (2025)

## Abstract (arXiv)

In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.