An approach of filtering the content of posts in social media

Abstract

Social media is playing a major role in relaxing, sharing thought for keep good relationship and academic purpose as well. But, there are some improper or demotivating posts available together, it makes parents to keep their children away from social media and them losing world knowledge. Therefore, keeping good security on content sharing should be considered and filtered out. Newsfeed is one the important portion on Facebook having attractive photos and text. Text of newsfeed posts are filtering out here by following a specific workflow. Text content is taken into the flow initially on feature extraction by non-textual features and replace them with relevant meaning for text processing. Later, the text is optimized by acronyms handling and removing stop words. Then, bad words available text content is separated using two type of dictionaries. By following a specific logic, those sent to classifier for identifying the meaning of the text. Support Vector Machine is used for achieving binary classification and output of this module is content can be viewed to user. For increasing its effective output, similarity among comments with the post is analyzed based on spam detection technique. Advertisements are focused as spam comment here. The system can be plugged with any social media for securing child user from demotivating posts.