The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Detecting opinion spams and fake news using text classification
Abstract
In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user‐generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n‐gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state‐of‐the‐art methods.
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Cites
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Cited by
Fake News Detection Using Machine Learning Ensemble Methods
Ahmad, I., Yousaf, M., Yousaf, S., & Ahmad, M. O. (2020) · Complexity · doi:10.1155/2020/8885861 · Cited by 474
An ensemble machine learning approach through effective feature extraction to classify fake news
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Fake news detection within online social media using supervised artificial intelligence algorithms
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A Comprehensive Review on Fake News Detection With Deep Learning
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Metadata
- Authors
- Ahmed, H., Traore, I., & Saad, S.
- Venue
- SECURITY AND PRIVACY
- Publisher
- Wiley
- Published
- 2017-12-29
- Volume
- 1
- Issue
- 1
- ISSN
- 2475-6725 (ISSN-L)
- OpenAlex
- W2778443828
- Cited by
- 521
- References
- 18
- FWCI
- 33.55
- Type
- journal article
- Language
- en
- Open access
- closed
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