An Application for Detecting and Avoiding Redundant Messages in Social Media

On social media we browse through enormous messages. Most of the messages tend to be redundant. Browsing through this redundancy is simply time consuming and attention seeking. The messages may consist of many day-to-day slangs and short forms that are ambiguous. The spectacular usability of the social media applications can be improved by overcoming redundancy. The standard form of slangs or short form words enhances the understanding ability of messages. This paper discusses an application that has been developed to avoid redundancy by standardizing the messages. The application deploys machine learning techniques to preprocess and analyze the messages. Henceforth, the redundant messages are restricted from appearing on the application user interface. By this the spectators get the unique and standard messages view.