This dataset contains a gold standard for the classification of users sharing the misinformation about COVID-19. It presents a list of mapped used id for privacy concerns, the list of real tweet id as retrieved from Twitter and the label classifying the tweet author as spreader or checker. Spreader are users supporting fake news, while checkers are users supporting real news. The list of fake and real news came from the CoAID dataset by Limeng and Dongwon.Data were retrieved from December 1, 2019 to April 1, 2021.For further details look at the paper "Fake News Spreader Automated Classification for Breaking the Misinformation Chain" in the MDPI Information Journal Special Issue "News Research in Social Networks and Social Media", or open an issue in the GitHub repository.

This is a publication of the CoAID dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents.

Cui, Limeng, et Dongwon Lee. 2020. « CoAID: COVID-19 Healthcare Misinformation Dataset ». ArXiv:2006.00885 [Cs], novembre. http://arxiv.org/abs/2006.00885.

In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.

Features are extracted using:

• A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1]

• A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1]

• A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) 3

• A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) 4

References:

[1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406

[2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410.

This is the text of the CoAID dataset dedicated to fake news detection that has been updated to be used in event detection.

Cui, Limeng, et Dongwon Lee. 2020. « CoAID: COVID-19 Healthcare Misinformation Dataset ». ArXiv:2006.00885 [Cs], novembre. http://arxiv.org/abs/2006.00885.

Guillaume Bernard. (2022). CoAID dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630405

Some degradations are applied using the DocCreator [1] tool in order to degrade the text of the tweets and to reproduce some common errors found in OCRised documents [2].

[1]: Journet, Nicholas, Muriel Visani, Boris Mansencal, Kieu Van-Cuong, et Antoine Billy. 2017. « DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images ». Journal of Imaging 3 (4): 62. https://doi.org/10.3390/jimaging3040062.

[2]: Linhares Pontes, Elvys, Ahmed Hamdi, Nicolas Sidere, et Antoine Doucet. 2019. « Impact of OCR Quality on Named Entity Linking ». In Digital Libraries at the Crossroads of Digital Information for the Future, 11853:102‑15. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-34058-2_11.

The results of the OCR degradations are as follow:

CoAID CER/WER




    Without
    Character degradation
    Phantom degradation
    Bleed
    Blur
    All


    CoAID
    CER
    2.105
    6.358
    2.105
    2.122
    2.616
    7.898


    CoAID
    WER
    2.494
    20.230
    2.496
    2.580
    3.726
    20.230