Fibvid dataset with multiple extracted features (both sparse and dense)

This is a publication of the FibVid 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.

Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». Telematics and Informatics 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688.

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.

[3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1

[4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2