Detecting cyber defamation in social network using machine learning
S. Jacob Standlin, K. Banuroopa · 2025
The amount of data generated on these platforms makes it challenging to detect and prevent cyber defamation manually. To address this challenge, this research paper proposes a machine learning-based approach to identify cyber defamation in social media. The approach leverages natural language processing techniques and machine learning algorithms to analyze social media posts’ text and classify them as defamatory or non-defamatory. Firstly, data is collect from various social media platforms and annotated for the presence of cyber defamation. Then, the data is preprocessed to clean and transform it into a suitable format for machine learning. Next, features are extracted from the data, such as the frequency of specific words or phrases, to create a representation of the text that machine learning algorithms can understand. These models are trained on the preprocessed and feature-extracted data to classify social media posts as defamatory or non-defamatory. The results of this research paper show that the proposed approach has achieved high accuracy in detecting cyber defamation in social media. The approach’s effectiveness is promising, as it could help social media platforms and law enforcement agencies identify and prevent cyber defamation more efficiently. The use of machine learning algorithms and natural language processing techniques could improve the accuracy and speed of detecting defamatory content online, potentially preventing the harmful impact of cyber defamation on individuals and organizations.