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Dataset . 2021
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Dataset . 2021
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Dataset . 2021
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WELFake dataset for fake news detection in text data

Authors: Verma, Pawan Kumar; Agrawal, Prateek; Prodan, Radu;

WELFake dataset for fake news detection in text data

Abstract

{"references": ["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."]}

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.

Keywords

text classification, machine learning, linguistic features, word embedding, fake news detection, Text news

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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