# WarCov -- Large multilabel and multimodal dataset from social platform

arXiv: https://arxiv.org/abs/2406.10255 · HTML: https://mlsanity.com/paper/2406.10255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [How Complex is your classification problem? A survey on measuring classification complexity](https://mlsanity.com/paper/1808.03591/) (2018)
- [This dataset does not exist: training models from generated images](https://mlsanity.com/paper/1911.02888/) (2019)
- [Will Large-scale Generative Models Corrupt Future Datasets?](https://mlsanity.com/paper/2211.08095/) (2022)
- [TWEETQA: A Social Media Focused Question Answering Dataset](https://mlsanity.com/paper/1907.06292/) (2019)
- [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://mlsanity.com/paper/2005.00630/) (2020)
- [MMChat: Multi-Modal Chat Dataset on Social Media](https://mlsanity.com/paper/2108.07154/) (2021)
- [NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2102.04567/) (2021)
- [NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/1904.01546/) (2019)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter](https://mlsanity.com/paper/2008.06465/) (2020)
- [MFAQ: a Multilingual FAQ Dataset](https://mlsanity.com/paper/2109.12870/) (2021)

## Abstract (arXiv)

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.