Abstract
Fake news is “news reports that are deliberatively and indisputably fake.” News that uses fake information is becoming a threat. It becomes challenging for humans to distinguish between fake and actual news. It has become necessary to detect fake news, which seeks to determine whether a news document can be believed. Detection of fake news faces challenges in accurate classification, making existing detection algorithms ineffective. In these issues, this article uses a novel Adaptive Capsule Transient Auto Encoder (ACTAE) for effectively detecting fake news. ACTAE is a combined approach of a classifier named Capsule Auto Encoder and an algorithm called Adaptive Transient Search Optimization Algorithm. The overall detection process is performed in various stages, including preprocessing, feature withdrawal, feature selection, and classification and optimization of weight parameters of the classifier for better results. The overall process is executed in Python, proving that ACTAE detects fake news with higher accuracy (99%) and lower error rate.
- [1] . 2020. FNDNet–a deep convolutional neural network for fake news detection. Cogn. Syst. Res. 61 (June 2020), 32–44.
DOI: Google ScholarDigital Library
- [2] . 2020. An overview of online fake news: Characterization, detection, and discussion. Inf. Process. Manag. 57, 2 (March 2020), 102025.
DOI: Google ScholarDigital Library
- [3] . 2017. The current state of fake news: Challenges and opportunities. Proc. Comput. Sci. 121 (January 2017), 817–825.
DOI: Google ScholarDigital Library
- [4] . 2020. Language-independent fake news detection: English, Portuguese, and Spanish mutual features. Fut. Internet 12, 5 (May 2020), 87.
DOI: Google ScholarCross Ref
- [5] . 2021. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimed. Tool. Appl. 80, 8 (January 2021), 11765–11788.
DOI: Google ScholarDigital Library
- [6] . 2021. A temporal ensembling-based semi-supervised ConvNet for the detection of fake news articles. Expert Syst. Appl. 177 (September 2021), 115002.
DOI: Google ScholarDigital Library
- [7] . 2023. A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection. Mathematics 11, 2 (January 2023), 258.
DOI: Google ScholarCross Ref
- [8] . 2019. Supervised learning for fake news detection. IEEE Intell. Syst. 34, 2 (May 2019), 76–81.
DOI: Google ScholarDigital Library
- [9] . 2021. Multiple features-based approaches for automatic fake news detection on social networks using deep learning. Appl. Soft Comput. 100 (March 2021), 106983.
DOI: Google ScholarCross Ref
- [10] . 2020. Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms. Expert Syst. Appl. 159 (November 2020), 113584.
DOI: Google ScholarCross Ref
- [11] . 2021. The linguistic feature-based learning model for fake news detection and classification. Expert Syst. Appl. 169 (May 2021), 114171.
DOI: Google ScholarCross Ref
- [12] . 2022. A comprehensive Benchmark for fake news detection. J. Intell. Inf. Syst. 59 (March 2022), 237–261.
DOI: Google ScholarDigital Library
- [13] . 2021. An ensemble machine learning approach through effective feature extraction to classify fake news. Fut. Gener. Comput. Syst. 117 (April 2021), 47–58.
DOI: Google ScholarCross Ref
- [14] . 2020. FNDNet–A deep convolutional neural network for fake news detection. Cogn. Syst. Res. 61 (June 2020), 32–44.
DOI: Google ScholarDigital Library
- [15] . 2020. Fake news detection within online social media using supervised artificial intelligence algorithms. Phys. A: Stat. Mech. Appl. 540 (February 2020), 123174.
DOI: Google ScholarCross Ref
- [16] . 2020. Text-mining-based fake news detection using ensemble methods. Int. J. Autom. Comput. 17, 2 (February 2020), 210–221.
DOI: Google ScholarCross Ref
- [17] . 2020. Fake news detection using a blend of neural networks: An application of deep learning. S. N. Comput. Sci. 1, 3 (May 2020), 1–9.
DOI: Google ScholarDigital Library
- [18] . 2020. A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. J. Supercomput. 76, 6 (June 2020), 4414–4429.
DOI: Google ScholarDigital Library
- [19] . 2021. An ensemble machine learning approach through effective feature extraction to classify fake news. Fut. Gener. Comput. Syst. 117 (April 2021), 47–58.
DOI: Google ScholarCross Ref
- [20] . 2022. Evaluating the effectiveness of publishers' features in fake news detection on social media. Multimed. Tool. Appl. 82 (April 2022), 2913--2939.
DOI: Google ScholarDigital Library
- [21] Retrieved from https://www.kaggle.com/competitions/fake-news/data.Google Scholar
- [22] Retrieved from https://github.com/KaiDMML/FakeNewsNet.Google Scholar
- [23] Retrieved from https://www.uvic.ca/ecs/ece/isot/datasets/fake-news/index.php.Google Scholar
- [24] . 2022. Automating fake news detection using PPCA and levy flight-based LSTM. Soft Comput. 26 (June 2022), 12545–12557.
DOI: Google ScholarDigital Library
- [25] . 2020. Fakenewsnet: A data repository with news content, social context and spatiotemporal information for studying fake news on social media. Big Data 8, 3 (June 2020), 171--188.
DOI: Google ScholarCross Ref
- [26] . 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor. Newslett. 19, 1 (June 2017), 22–36.
DOI: Google ScholarDigital Library
- [27] . 2017. Exploiting tri-relationship for fake news detection. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM'17). Melbourne VIC, arXiv preprint arXiv:1712.07709.Google Scholar
- [28] . 2017. Detecting opinion spams and fake news using text classification. J. Secur. Priv. 1, 1 (December 2017) 2.
DOI: Google ScholarCross Ref
- [29] . 2017. Detection of online fake news using N-gram analysis and machine learning techniques. In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments (ISDDC’17). Lecture Notes in Computer Science, I. Traore, I. Woungang, and A. Awad (Eds.). Springer, Cham, 10618. Google Scholar
Cross Ref
- [30] . 2022. AI-based misogyny detection from Arabic Levantine Twitter tweets. Comput. Sci. Math. Forum. 2, 1 (September 2021), 15.
DOI: Google ScholarCross Ref
- [31] . 2022. Interpretable rake news detection with topic and deep variational models (September 2022). arXiv:2209.01536.Google Scholar
- [32] . 2017. Fuzzy bag-of-words model for document representation. IEEE Trans. Fuzzy Syst. 26, 2 (March 2017), 794–804.
DOI: Google ScholarCross Ref
- [33] . 2020. I am automating a fake news detection system using the multi-level voting model. Soft Comput. 24, 12 (June 2020), 9049–9069.
DOI: Google ScholarDigital Library
- [34] . 2018. A tool for fake news detection. In Proceedings of the 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’18). IEEE, Los Alamitos, CA, 379–386.
DOI: Google ScholarCross Ref
- [35] . 2021. OPCNN-FAKE: Optimized convolutional neural network for fake news detection. IEEE Access 9 (September 2021), 129471–129489.
DOI: Google ScholarCross Ref
- [36] . 2010. Jun. Learning to rank with (a lot of) word features. Inf. Retr. 13, 3 (June 2010), 291–314.
DOI: Google ScholarDigital Library
- [37] . 2019. The bitwise Hashing trick for personalized search. Appl. Artif. Intell. 33, 9 (July 2019), 829–837.
DOI: Google ScholarCross Ref
- [38] . 2021. Improved equilibrium optimization algorithm using elite opposition-based learning and new local search strategy for feature selection in medical datasets. Computation 9, 6 (June 2021), 68.
DOI: Google ScholarCross Ref
- [39] . 2021. Domestic activities clustering from audio recordings using convolutional capsule autoencoder network. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’21). IEEE, 835–839.
DOI: Google ScholarCross Ref
- [40] . 2021. An improved transient search optimization with dimensional neighbourhood learning for global optimization problems. Symmetry 13, 2 (February 2021), 244.
DOI: Google ScholarCross Ref
- [41] . 2022. MCred: multi-modal message credibility for fake news detection using BERT and CNN. J Amb. Intell. Human. Comput. 27 (July 2022), 1–3.
DOI: Google ScholarCross Ref
- [42] . 2020. Automating fake news detection system using the multi-level voting model. Soft Comput. 24, 12 (June 2020), 9049–9069.
DOI: Google ScholarDigital Library
- [43] . 2021. A temporal ensembling-based semi-supervised ConvNet for the detection of fake news articles. Expert Syst. Appl. 177 (September 2021), 115002.
DOI: Google ScholarDigital Library
- [44] . 2020. FNDNet–a deep convolutional neural network for fake news detection. Cogn. Syst. Res. 61 (June 2020), 32–44.
DOI: Google ScholarDigital Library
- [45] . 2022. A heuristic-driven uncertainty-based ensemble framework for fake news detection in tweets and news articles. Neurocomputing 491 (June 2022), 607–620.
DOI: Google ScholarDigital Library
- [46] . 2021. DeepFakE: improving fake news detection using tensor decomposition-based deep neural network. J. Supercomput. 77, 2 (February 2021), 1015–1037.
DOI: Google ScholarCross Ref
- [47] . 2021. Fakeflow: Fake news detection by modelling the flow of affective information. arXiv preprint arXiv:2101.09810.Google Scholar
- [48] . 2022. A novel self-learning semi-supervised deep learning network to detect fake news on social media. Multimed. Tool. Appl. 81, 14 (June 2022), 19341–19349.
DOI: Google ScholarDigital Library
- [49] . 2021. A novel stacking approach for accurate detection of fake news. IEEE Access 9 (February 2021), 22626–22639.
DOI: Google ScholarCross Ref
- [50] . 2021. Detecting fake news with capsule neural networks. Appl. Soft Comput. 101 (March 2021), 106991.
DOI: Google ScholarDigital Library
- [51] . 2022. Deep ensemble fake news detection model using sequential deep learning technique. Sensors 22, 18 (September 2022), 6970.
DOI: Google ScholarCross Ref
- [52] . 2022. Automating fake news detection using PPCA and levy flight-based LSTM. Soft Comput. 26, 22 (June 2022), 12545–12557. Google Scholar
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Index Terms
An Efficient and Accurate Detection of Fake News Using Capsule Transient Auto Encoder
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