WarCov -- Large multilabel and multimodal dataset from social platform
<|reference_start|>WarCov -- Large multilabel and multimodal dataset from social platform: In the classification tasks, from raw data acquisition to the curation of a dataset suitable for use in evaluating machine learning models, a series of steps - often associated with high costs - are necessary. In the case of Natural Language Processing, initial cleaning and conversion can be performed automatically, but obtaining labels still requires the rationalized input of human experts. As a result, even though many articles often state that "the world is filled with data", data scientists suffer from its shortage. It is crucial in the case of natural language applications, which is constantly evolving and must adapt to new concepts or events. For example, the topic of the COVID-19 pandemic and the vocabulary related to it would have been mostly unrecognizable before 2019. For this reason, creating new datasets, also in languages other than English, is still essential. This work presents a collection of 3~187~105 posts in Polish about the pandemic and the war in Ukraine published on popular social media platforms in 2022. The collection includes not only preprocessed texts but also images so it can be used also for multimodal recognition tasks. The labels define posts' topics and were created using hashtags accompanying the posts. The work presents the process of curating a dataset from acquisition to sample pattern recognition experiments.<|reference_end|>
@article{borek-marciniec2024warcov,
    title={WarCov -- Large multilabel and multimodal dataset from social platform},
    author={Weronika Borek-Marciniec, Pawel Zyblewski, Jakub Klikowski, Pawel
  Ksieniewicz},
    journal={arXiv preprint arXiv:2406.10255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10255},
    primaryClass={cs.CL cs.SI}
}