Dataset Open Access

WELFake dataset for fake news detection in text data

Pawan Kumar Verma; Prateek Agrawal; Radu Prodan

Contact person(s)
Agrawal, Prateek; Verma, Pawan K
Data collector(s)
Verma, Pawan K
Project leader(s)
Prodan, Radu
Supervisor(s)
Prodan, Radu; Agrawal, Prateek

We designed a larger and more generic Word Embedding over Linguistic Features for Fake News Detection (WELFake) dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, we merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training.

Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real).

There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame.

 

This dataset is a part of our ongoing research on "Fake News Prediction on Social Media Website" as a doctoral degree program of Mr. Pawan Kumar Verma and is partially supported by the ARTICONF project funded by the European Union’s Horizon 2020 research and innovation program.

Files (245.1 MB)
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WELFake_Dataset.csv
md5:73c9675a4b3d09f86a6933d0b8d7d908
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  • Benjamin political news dataset, https://github.com/rpitrust/fakenewsdata1, Accessed: 31 March 2020.

  • Burfoot satire news dataset, http://www.csse.unimelb.edu.au/research/lt/ resources/satire, Accessed: 31 March 2020.

  • Buzzfeed news dataset, https://github.com/BuzzFeedNews/2016-10-facebook-fact-check/tree/master/data, Accessed: 31 March 2020.

  • Credbank dataset, http://compsocial.github.io/CREDBANK-data, Accessed: 31 March 2020.

  • Fake news challenge dataset, https://github.com/FakeNewsChallenge/fnc-1, Accessed: 31 March 2020.

  • Fakenewsnet dataset, https://github.com/KaiDMML/FakeNewsNet, Accessed: 31 March 2020.

  • Liar dataset, https://www.cs.ucsb.edu/~william/data/liar\_dataset.zip, Accessed:31 March 2020.

  • Verma P.K., Agrawal P. (2020). Study and Detection of Fake News: P2C2-Based Machine Learning Approach, International Conference on Data Management, Analytics and Innovation, pp. 261-278, Delhi. https://doi.org/10.1007/978-981-15-5619-7_18

  • P. K. Verma, P. Agrawal, I. Amorim and R. Prodan, "WELFake: Word Embedding Over Linguistic Features for Fake News Detection," IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2021.3068519.

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