Identifying Social Network Delusion to Investigate Addiction Ratio using Data Mining

Abstract

Mining social media is the process of defining, analyzing, and extracting applicative patterns and trends from row social media data. Social media are very popular way of expressing opinions and interacting with many individual in the online world. However growing number of social network delusion among various age categories are recently noted. Mental sickness can have a deep influence on person, families, and society as well. Hence, we propose a framework that analyzes Social Network Delusion (SND) and investigates the addiction ratio. This work first defines the framework for analyzing the social network delusion based on mining online social behavior that provides an early stage opportunity to identify SNDs (Social Network Delusion). The proposed system mainly works in three phases. Feature extraction and analysis of the various posts posted by the users on Facebook, Instagram and Twitter is performed by using mining algorithm in the first step. The SND prediction using the extracted features is done in the second phase; Third phase uses the predicted results as an input for investigating the addiction ratio. We investigate the addiction ratio among different genders and age groups for analyzing the prevention strategies against growing number of SND.