Many application systems have been created and a lot of research has been conducted to detect suspicious word messages, chats, profiles, email spam, malicious web content URLs and also to detect phishing attacks in social media. The social approach to detect spam or harmful content on Facebook, Twitter, Myspace is based on information found in social media by identifying or detecting malicious URL links, emails with spam content, suspicious words from images and video text. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay There are few applications that can detect suspicious words embedded in images or videos with the help of image processing and retrieval techniques. Previous study on tracking messages sent via social networking sites and instant messages. The designed framework that prevents, predicts and provides profile evidence of cyber attacks when suspicious messages are sent between users, but fails to detect suspicious short-form and hard-word messages sent via IM and SNM in real time. They also fail to detect suspicious words in all types of long and short words and encoded words embedded in image contents. Mohd Mahmood Ali, Khaja Moizuddin Mohd and Lakshmi Rajamani are researchers who discovered the SMD framework to detect suspicious words from messages stored in the database after users made communications via social media{Ali2014}. This document did not focus on code words and short-form chat messages. The aim was to prevent, predict and provide evidence of suspicious words and profile an individual or group committing crimes and report it to the E-crime department. However, the used approaches such as data mining and ontological structure have semantically divided the website texts with the assistance of the Word Net database into different attributes of threat categories, for example: murder, kidnapping and sexual violence. But the ontology was not updated frequently with new code words discovered using the data mining approach. Rajamani, Lakshmi Ali, Mohammed Mahmood Rasheed, Mohammed Abdul presented a system designed with secure text structure thinking that recognizes suspicious messages causing illegal movements by criminals. One framework did not focus on protecting messages using encryption approaches and also did not focus on short-form messages. This article provides different thoughts regarding the stemming calculation and the priority algorithm. Murugesan, Devi, Deepthi, Lavanya and Annie Princy. They proposed that the system automatically monitor suspicious discussions on online forums and used text analysis to detect suspicious posts in online forums. They focus on automated classification to identify the most prominent suspicious discussions{Murugesan2016}. Thivya Shilpa. Gv proposed a framework that provides security, predicts, detects code words and short forms of suspicious words with the help of association rule mining techniques and ontology concepts that provide security for chat messages stored using cryptographic techniques . But this document does not detect suspicious words attached to image contents. Salim Almi Omar Beqqali, a researcher, discovered an automatic system to detect suspicious profiles in social media by identifying suspicious performance and user concerns. The presented techniques are mainly based on calculating similarity distance to differentiate suspicious posts using text analytics. The limitations encountered are execution times, development of .