Machine Learning-Based Detection of Cyber Defamation in Social Networks - Tharanidharan, Sridevi; Al-Makki, Nahla Al-Nour Muhammad; Kumar, Nishi Kant; Boddukoori, Divya; Sharma, Himanshu; Pokhariya, Hemant Singh; Shrivastava, Anurag (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Cyber defamation, or the act of making false and harmful statements about individuals or organizations online, has become a prevalent issue in social networks. To safeguard the reputation and well-being of people and entities, cyber defamation must be identified and addressed. It suggests using machine learning to detect online slander in social networks. It involves collecting a dataset of social media posts and comments that have been reported or flagged as potentially defamatory. It preprocesses the textual data by removing noise, performing tokenization, and applying techniques such as stemming or lemmatization. Next, we extract relevant features from the text including linguistic patterns, sentiment and contextual information. It develops a machine learning model, such as a Support Vector Machine (SVM) or a deep learning model, such as a Recurrent Neural Network (RNN), using the preprocessed data and extracted features. The programme is trained to distinguish between defamatory and non-defamatory social media posts and comments. The results of the studies show how well our method works for spotting cyberbullying in social media. It delivers a high level of accuracy in recognising defamatory content by utilising machine learning techniques, enabling quick intervention and mitigation of the harm caused by such content. The findings have significant implications for online reputation management, social media platforms, and individuals or organizations targeted by cyber defamation. Detecting and addressing defamatory content in a timely manner can protect individuals' reputations, maintain a positive online environment, and contribute to the well-being of users in social networks. Moving forward, further research can focus on enhancing the model's performance by incorporating additional contextual features, exploring ensemble methods, or considering multilingual and cross-platform settings. By continuously improving cyber defamation detection systems, it can foster safer and more respectful online communities.