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.
Published In:IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 702-707
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Dwarapu Suneetha : Research Scholar, Department of CSE, JNTU Kakinada, India.
Mogalla Shashi : Professor, Department of CS &SE, Andhra University, Visakhapatnam, India.
Social Media Analytics, Sentiment Analysis, Minimum Bounding Similarity, Content analytics, Patterns,
Knowledge
According to Pew Research Center on Internet
Science & Technology news bulletin dated 8th Oct
’15, 65% adults are using social networking sites
which is a tenfold increase in the last decade.
Since lot of data is getting accumulated in social
networks, they provide a rich source for data mining
researchers to extract hidden patterns and
knowledge useful to various domains. This paper
proposes an overview of various concepts and
measures related to social network analysis. It also
through light on the recent research outcomes and
applications of social network analytics.
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