Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network
Source abstract
Abstract
In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic. The initial feature extraction process is done using a convolutional recurrent neural network (CRNN). After the extraction of features, word indexing is done with high dimensionality. Then, based on the indexing measures, the ranking process identifies whether news is fake or real. The fuzzy CRNN model is trained to yield outstanding results with 99.99 ± 0.01% accuracy. This work utilizes three different datasets (LIAR, LIAR-PLUS, and ISOT) to find the most accurate model.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Spam and Phishing Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. Analysis of classifiers for fake news detection10.1016/j.procs.2020.01.035 ↗
2. Fake news detection using Bi-directional LSTM-recurrent neural network10.1016/j.procs.2020.01.072 ↗
3. Detecting fake news for reducing misinformation risks using analytics approaches10.1016/j.ejor.2019.06.022 ↗
4. Fake news identification and classification using DSSM and improved recurrent neural network classifier10.1080/08839514.2019.1661579 ↗
5. Fake news stance detection using a stacked ensemble of classifiers
6. An innovative topic-based customer complaints sentiment classification system10.1504/IJBIR.2019.102718 ↗
7. Classifying fake news articles using natural language processing to identify in-article attribution as a supervised learning estimator
8. A roadmap towards implementing parallel aspect level sentiment analysis10.1007/s11042-018-7093-z ↗
9. J. Zhang, L. Cui, Y. Fu and F. B. Gouza, “Fake news detection with the deep diffusive network model,” arXiv preprint arXiv abs/1805.08751, 2018.
10. Content-based fake news detection using knowledge graphs
11. Towards automatic fake news classification10.1002/pra2.2018.14505501125 ↗
12. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks
13. Eann: event adversarial neural networks for multi-modal fake news detection
14. Tracing fake-news footprints: characterizing social media messages by how they propagate
15. Fake news tracker: A tool for fake news collection, detection, and visualization10.1007/s10588-018-09280-3 ↗
16. Online public shaming on twitter: detection, analysis, and mitigation10.1109/TCSS.2019.2895734 ↗
17. A supervised aspect level sentiment model to predict overall sentiment on tweeter documents10.1504/IJMSO.2018.096451 ↗
18. Unsupervised fake news detection on social media: A generative approach
19. F. Monti, F. Frasca, D. Eynard, D. Mannion and M. M. Bronstein, “Fake news detection on social media using geometric deep learning,” arXiv preprint arXiv: 1902.06673, 2019.
20. Multi-source multi-class fake news detection
21. Parsing based sarcasm detection from literal language in tweets10.2174/2213275911666180531112306 ↗
22. Sentiment analysis leveraging emotions and word embeddings10.1016/j.eswa.2016.10.043 ↗
23. Detection of online fake news using n-gram analysis and machine learning techniques
24. Y. Yang, L. Zheng, J. Zhang, Q. Cui, Z. Li et al., “TI-CNN: convolutional neural networks for fake news detection,” arXiv preprint arXiv:1806.00749, 2018.
25. The language of fake news: opening the black-box of deep learning based detectors
26. FNDNet–a deep convolutional neural network for fake news detection10.1016/j.cogsys.2019.12.005 ↗
27. Optimization of neural network using improved bat algorithm for data classification10.1166/jmihi.2019.2654 ↗
28. A fuzzy convolutional neural network for text sentiment analysis10.3233/JIFS-169843 ↗
29. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews10.1016/j.eij.2021.03.001 ↗
30. An approach using fuzzy sets and boosting techniques to predict liver disease10.32604/cmc.2021.016957 ↗
31. W. Y. Wang, “Liar, liar pants on fire”: A new benchmark dataset for fake news detection,” arXiv preprint arXiv: 1705.00648, pp. 422–426, 2017.10.18653/v1/P17-2067 ↗
32. Where is your evidence: improving fact-checking by justification modeling
33. Fake news detection: A hybrid CNN-RNN based deep learning approach10.1016/j.jjimei.2020.100007 ↗
34. Thyroid disease prediction using machine learning approaches10.1007/s40009-020-00979-z ↗