Research Issues and Developments in Social Network Analytics

Abstract

-As billions of individuals share data, opinions, images, videos, through social media, enormous data is getting accumulated attracting researchers towards Social Network Analytics which involves combination of structural and content analytics to mine patterns/knowledge from social media. This paper provides a comprehensive survey of various concepts, challenges, techniques, and outcomes of recent research on Social Media Analytics. The major research issues including partial information, scalability, heterogeneity, structural component and dynamically changing content call for specifically designed techniques for representation and analysis of social media content. Different types of node centralities to quantify the impact of an individual in social media are discussed. This paper provides useful insights on different types of network structures and models for propagation of opinions/influence in different types of networks. It also provides inputs on latest methods for dynamically changing link prediction and visualization for better understanding. The paper discusses successful methods for Sentiment Analysis. It also discusses the concept of Socio dimensions to identify the heterogeneity of connections between nodes of a social network to accurately predict the interests of an individual for targeted marketing etc. Recent research on dynamic topic detection from tweet stream based on a predefined threshold on Minimum Bounding Similarity is discussed. Extensive reference material on related concepts and techniques are briefly discussed in this paper and the citations are helpful for further readings.