SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles
This work addresses the problem of automated satire detection for Romanian language applications, providing a foundational dataset and benchmarks, though it is incremental as it applies existing methods to new data.
The authors tackled satire detection in Romanian news by creating a novel corpus of 55,608 articles, the largest for this task across languages and the only one for Romanian, and established baselines with deep neural models achieving up to 73% accuracy, significantly below human performance of 87%.
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.