Raghunathan, KeerthikantSyed, DabeeruddinZainab, AmeemaVuppala, MounikaOzhan, DavutRefaat, Shady S.2025-10-152025-10-152025979833151197597983315119682837-4932https://doi.org/10.1109/SMARTNETS65254.2025.11106844https://hdl.handle.net/20.500.12514/9823Social media platforms have become a common ground to induce political discussions and manipulating public opinion. This has led to the inception of trolling that involves diverting public opinion from facts, driving people into emotionally charged and appealing discussions, and spreading disinformation against an organization, entity or a country for political or other gains. Trolling is conducted by humans or programmed bots. In the recent past, there have been numerous manipulative and malicious campaigns to spread fake news about different nations and economies on social media. Rather than focusing on individual accounts, it is crucial to discover manipulative campaigns. This involves challenges such as recognizing complex patterns, computational complexity, and real-time performance. Our proposed solution of real-time trolls identification automation is based on social media analytics using deep machine learning. Real-time ability is achieved using text stream clustering, and the design approach is evaluated on real-world tweets. The current performed work utilizes fine tuning large language models to apprehend a higher degree of complexity and employs a distilled form of Bidirectional Encoder Representations from Transformers models to obtain high accuracy of detection. A correspondence analysis is beneficial to map noun-verb relationships in structured data. With the proposed approach, an accuracy of 92% was achieved.en10.1109/SMARTNETS65254.2025.11106844info:eu-repo/semantics/closedAccessBidirectional Encoder Representations From TransformersDeep LearningDistilbertSocial Media AnalyticsTrolls IdentificationConference Object2-s2.0-105015525561