LAUSR

The Social, Mobile, Analytics and Cloud (SMAC) explosion in recent times changed the way the customers look at and collaborate businesses through large world of data or information, often described as “Big Data”. With over 1590 million active users, social media such as Facebook, Twitter, WhatsApp, Instagram, LinkedIn etc. send or receive messages or post or access new content every day. Businesses or enterprises understand and extract useful insights from social media platform and transforming it into useful information or knowledge along with their enterprise business data for strategic decision making. A framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model is proposed for social media data and our comprehensive study on large-scale twitter dataset experiment explains how MCDM (TOPSIS) method outperforms against the standard centrality methods using predicted Spreading Size. Two well-known metrics such as MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) are applied to measure prediction accuracy of our MCDM based method against standard methods. TOPSIS (MCDM based) experiences least error accuracy of 9% in MAE and 16% in RMSE than the standard methods to prove that the proposed approach works better than the standard methods.