Deep Learning Based Detection and Classification of Anomaly Texts in Social Media
Keywords:
Deep Machine, Twitter, Anomaly BehaviorAbstract
The Social Media (SM) not only plays a significant role in the process of connecting people from different parts of the world, but it also offers a multitude of opportunities for the extraction of knowledge. This is in addition to the fact that the SM plays a significant role in the process of connecting people from different parts of the world. It is not a straightforward process at this moment to provide an answer to the question of how to extract information from data and gain knowledge from this data. The advancement of techniques for machine learning and the growth in the amount of computer power that is easily available made it possible, in part, to make use of the latent value that is included in this data. In this paper, various machine learning models are integrated with deep learning to detect and classify the anomaly text in social media applications. We provide a deep machine learning technique to scanning Twitter for unusual behavior. This method takes into account not just the textual material that individuals publish on Twitter but also the relationships between those users. This strategy is predicated on the idea that a user data choice for a social network should be congruent with their regular behaviors or those of other users with profiles that are comparable to their own.
Downloads
References
Nawaratne, R., Alahakoon, D., De Silva, D., & Yu, X. (2019). Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics, 16(1), 393-402.
Garg, S., Kaur, K., Kumar, N., Kaddoum, G., Zomaya, A. Y., & Ranjan, R. (2019). A hybrid deep learning-based model for anomaly detection in cloud datacenter networks. IEEE Transactions on Network and Service Management, 16(3), 924-935.
Islam, M., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems, 6(1), 1-12.
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., ... & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76, 243-297.
Moradi Vartouni, A., Teshnehlab, M., & Sedighian Kashi, S. (2019). Leveraging deep neural networks for anomaly‐based web application firewall. IET Information Security, 13(4), 352-361.
Narayanan, K. L., Krishnan, R. S., Robinson, Y. H., Julie, E. G., Vimal, S., Saravanan, V., & Kaliappan, M. (2022). Banana plant disease classification using hybrid convolutional neural network. Computational Intelligence and Neuroscience, 2022.
Kim, M. (2019). Supervised learning‐based DDoS attacks detection: Tuning hyperparameters. ETRI Journal, 41(5), 560-573.
Gunjan, V. K., Vijayalata, Y., Valli, S., Kumar, S., Mohamed, M. O., & Saravanan, V. (2022). Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies. Computational Intelligence and Neuroscience, 2022.
Yadav, B. P., Ghate, S., Harshavardhan, A., Jhansi, G., Kumar, K. S., & Sudarshan, E. (2020, December). Text categorization Performance examination Using Machine Learning Algorithms. In IOP Conference Series: Materials Science and Engineering (Vol. 981, No. 2, p. 022044). IOP Publishing.
Kousik, N. V., Sivaram, M., Yuvaraj, N., & Mahaveerakannan, R. (2021). Improved density-based learning to cluster for user web log in data mining. In Inventive Computation and Information Technologies (pp. 813-830). Springer, Singapore.
Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82.
Karthick, S., Yuvaraj, N., Rajakumari, P. A., & Raja, R. A. (2021). Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence. In Intelligent Computing and Applications (pp. 549-557). Springer, Singapore.
Al-Makhadmeh, Z., & Tolba, A. (2020). Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach. Computing, 102(2), 501-522.
Daniel, A., Bharathi Kannan, B., Yuvaraj, N., & Kousik, N. V. (2021). Predicting Energy Demands Constructed on Ensemble of Classifiers. In Intelligent Computing and Applications (pp. 575-583). Springer, Singapore.
Khan, J. Y., Khondaker, M., Islam, T., Iqbal, A., & Afroz, S. (2019). A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749, 1-14.
Zad, S., Heidari, M., Jones, J. H., & Uzuner, O. (2021, May). A survey on concept-level sentiment analysis techniques of textual data. In 2021 IEEE World AI IoT Congress (AIIoT) (pp. 0285-0291). IEEE.
Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76(1), 139-154.
Guo, Z., Tang, L., Guo, T., Yu, K., Alazab, M., & Shalaginov, A. (2021). Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Future generation computer systems, 117, 205-218.
Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., ... & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering.
Asif, M., Ishtiaq, A., Ahmad, H., Aljuaid, H., & Shah, J. (2020). Sentiment analysis of extremism in social media from textual information. Telematics and Informatics, 48, 101345.
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2021). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management.
Abavisani, M., Wu, L., Hu, S., Tetreault, J., & Jaimes, A. (2020). Multimodal categorization of crisis events in social media. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14679-14689).
Neupane, D., & Seok, J. (2020). A review on deep learning-based approaches for automatic sonar target recognition. Electronics, 9(11), 1972.
Florio, K., Basile, V., Polignano, M., Basile, P., & Patti, V. (2020). Time of your hate: The challenge of time in hate speech detection on social media. Applied Sciences, 10(12), 4180.
Natarajan, Y., Kannan, S., & Mohanty, S. N. (2021). Survey of Various Statistical Numerical and Machine Learning Ontological Models on Infectious Disease Ontology. Data Analytics in Bioinformatics: A Machine Learning Perspective, 431-442.
Guha, A., & Samanta, D. (2021). Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance. International Journal of Automation and Computing, 18(1), 55-72.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
