The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
An ensemble machine learning approach through effective feature extraction to classify fake news
Abstract
Abstract There are numerous channels available such as social media, blogs, websites etc., through which people can easily access the news. It is due to the availability of these platforms that the dissemination of the fake news has become easier. Anyone using these platforms can create and share the fake news content based on personal or professional motives. To address the issue of detecting fake news, numerous studies based on supervised and unsupervised learning methods have been proposed. However, all those studies do suffer from a certain limitation of poor accuracy. The reason for poor accuracy can be attributed due to several reasons such as poor selection of features, inefficient tuning of parameters, imbalanced datasets etc. In this article, we have proposed an ensemble classification model for detection of the fake news that has achieved a better accuracy compared to the state-of-the-art. The proposed model extracts important features from the fake news datasets and the extracted features are then classified using the ensemble model comprising of three popular machine learning models namely, Decision Tree, Random Forest and Extra Tree Classifier. We achieved a training accuracy of 99.8% and testing accuracy of 44.15% on ISOT dataset and training and testing accuracy of 100% on Liar dataset.
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Cites
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
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Influence of fake news in Twitter during the 2016 US presidential election
Bovet, A., & Makse, H. A. (2019) · CUNY Academic Works (City University of New York) · Cited by 882
Analysis of Dimensionality Reduction Techniques on Big Data
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020) · IEEE Access · doi:10.1109/access.2020.2980942 · Cited by 877
Influence of fake news in Twitter during the 2016 US presidential election
Bovet, A., & Makse, H. A. (2019) · Nature Communications · doi:10.1038/s41467-018-07761-2 · Cited by 701
Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques
Ahmed, H., Traore, I., & Saad, S. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-69155-8_9 · Cited by 658
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., & Mishra, S. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2543145 · Cited by 547
Detecting opinion spams and fake news using text classification
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Research works printed but not linked: 3
- Shah, 2019, Human behavior classification using geometrical features of skeleton and support vector machines, Comput. Mater. Continua, 61, 535, 10.32604/cmc.2019.07948 · doi:10.32604/cmc.2019.07948
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Cited by
Fake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772
A Comprehensive Review on Fake News Detection With Deep Learning
Mridha, M. F., Keya, A. J., Hamid, M. A., Monowar, M. M., & Rahman, M. S. (2021) · IEEE Access · doi:10.1109/access.2021.3129329 · Cited by 281
Carrasco-Farré, C. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01174-9 · Cited by 84
Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning
Alwhbi, I. A., Zou, C. C., & Alharbi, R. N. (2024) · Sensors · doi:10.3390/s24113509 · Cited by 81
“The research conducted by Hakak et al. [74] is an example of work showing the importance of feature extraction techniques in identifying fake news more efficiently.”
BackgroundSoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
Singhal, M., Ling, C., Paudel, P., Thota, P., Kumarswamy, N., Stringhini, G., & Nilizadeh, S. (2023) · 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp57164.2023.00056 · Cited by 57
Metadata
- Authors
- Hakak, S., Alazab, M., Khan, S., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 117
- Pages
- 47-58
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3107058614
- Cited by
- 425
- References
- 37
- FWCI
- 83.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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- Hakak, S., Alazab, M., Khan, S., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z. (2021). An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2020.11.022
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