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Behind the cues: A benchmarking study for fake news detection · Study

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ArticleBehind the cues: A benchmarking study for fake news detection

Georgios Gravanis2019

Article2019

Behind the cues: A benchmarking study for fake news detection

Georgios Gravanis · Athena I. Vakali · Konstantinos Diamantaras · Panagiotis Karadais

233 citations·0/0 files stored·Expert Systems with Applications

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Misinformation and Its Impacts Spam and Phishing Detection Hate Speech and Cyberbullying Detection

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2017Social Media and Fake News in the 2016 Election 2009The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods 2017Fake News Detection on Social Media 2003Lying Words: Predicting Deception from Linguistic Styles 2015Automatic deception detection: Methods for finding fake news 2017This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in Text Body, More Similar To Satire Than Real News 2017Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques

Cited by

2023Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review 2020Fake news detection in multiple platforms and languages 2021Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection 2020Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study 2021AMFB: Attention based multimodal Factorized Bilinear Pooling for multimodal Fake News Detection

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Details

Year2019

Typearticle

Published2019-03-21

VenueExpert Systems with Applications

ISSN-L0957-4174

Languageen

Citations233

Files0 public / 0

ShelfMisinformation and Its Impacts

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doi10.1016/j.eswa.2019.03.036

openalex_workW2924988155

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