This dataset was introduced in the forthcoming paper below:
@inproceedings{ashraf-2024-DefaktS:,
address = {Torino, Italia},
title = {DeFaktS: A Fine-Grained Dataset for Analyzing Disinformation in German Media
},
booktitle = {Proceedings of The 2024 Joint International Conference, on Computational Linguistics, Language Resources and Evaluation},
publisher = {European Language Resources Association},
author = {Ashraf, Shaina and Bezzaoui, Isabel and Andone, Ionut and Markowetz, Alexander and Fegert, Jonas and Flek, Lucie},
month = may,
year = {2024},
abstract = {
In today’s rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset “DeFaktS”, designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing 105,855 posts with 20,008 meticulously labeled tweets, serves as a rich platform for in-depth exploration of disinformation’s diverse characteristics. A key attribute that sets DeFaktS apart is its fine-grained annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of disinformation content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding. In our experiments, we employed a mix of classical machine learning and advanced transformer-based models. The results underscored the potential of DeFaktS, with transformer models, especially the German variant of BERT, exhibiting pronounced effectiveness in both binary and fine-grained classifications.
}
}
If you have any questions, you can contact here: ashrafs@staff.uni-marburg.de, sashraf@bit.uni-bonn.de