The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Supervised Learning for Fake News Detection
Abstract
A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.
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Citations
Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1×
“These features have been extensively used in previous efforts,(12) especially to better understand the phenomenon of fake news.(13) Next, we detail the 21 features from this category.”
MethodLazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
Linguistic Inquiry and Word Count (LIWC): Pronounced “Luke,” . . . and Other Useful Facts
Chung, C. K., & Pennebaker, J. W. (2012) · Applied Natural Language Processing · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,151
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2067 · Cited by 1,516
Automatic deception detection: Methods for finding fake news
Conroy, N. K., Rubin, V. L., & Chen, Y. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010082 · Cited by 979
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · RePEc: Research Papers in Economics · Cited by 574
Sentiment Analysis Is a Big Suitcase
Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.4531228 · Cited by 439
Volkova, S., Shaffer, K., Jang, J. Y., & Hodas, N. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2102 · Cited by 381
Cited by
Sahoo, S. R., & Gupta, B. B. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106983 · Cited by 441
An ensemble machine learning approach through effective feature extraction to classify fake news
Hakak, S., Alazab, M., Khan, S., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.11.022 · Cited by 425
Detecting fake news by exploring the consistency of multimodal data
Xue, J., Wang, Y., Tian, Y., Li, Y., Shi, L., & Wei, L. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102610 · Cited by 298
Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review
Capuano, N., Fenza, G., Loia, V., & Nota, F. D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.005 · Cited by 183
Effects of Credibility Indicators on Social Media News Sharing Intent
Yaqub, W., Kakhidze, O., Brockman, M. L., Memon, N., & Patil, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376213 · Cited by 171
“In fact, several techniques that attempt to detect fake news via automated means are based on AI [7,31,42,47,52,56].”
Background
Metadata
- Authors
- Reis, J. C. S., Correia, A., Murai, F., Veloso, A., & Benevenuto, F.
- Venue
- IEEE Intelligent Systems
- Published
- 2019-03
- Volume
- 34
- Issue
- 2
- Pages
- 76-81
- ISSN
- 1541-1672 (ISSN-L)
- 1941-1294
- OpenAlex
- W2944575651
- Cited by
- 521
- References
- 13
- FWCI
- 131.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Reis, J. C. S., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Supervised Learning for Fake News Detection. IEEE Intelligent Systems. https://doi.org/10.1109/mis.2019.2899143
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