Deep Learning Based Detection and Classification of Anomaly Texts in Social Media - Jayadurga, R.; Veeramakali, T.; Ali Sohail, Mohammed; Alangudi Balaji, N.; Kumar C., Kiran; L. P., Sajitha (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.