Recognition Based Mechanism to Detect Malicious Content in Social Networks

In the past six years, tremendous growth in the size and popularity of social networking has fundamentally changed the way to use the Internet. Online social media services like Facebook and Twitter witness an exponential increase in user activity when an event takes place in the real world. This activity is a combination of good quality content like information, personal views, opinions, comments, as well as poor quality content like rumors, spam, and other malicious contents. Although the best quality contents makes online social media a rich source of information, consumption of poor quality content can degrade the user experience and have an inappropriate impact in the real world. In this paper, we propose a new approach to detect malicious content on social networks, while using the module of filtering processes by content, to calculate the frequencies of the content entered into parameter and extract the entire contents which are similar to the chosen content to classify them in two classes {malicious content, legitimate content}. The recognition module will be the subject of performing the phase of spread that is to make the learning of malicious content already inserted in a database, the phase of back propagation is the recognition of content and their similar given by filtering by content.