Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Social Networking Services (SNSs) connect people worldwide, where they communicate through sharing contents, photos, videos, posting their first-hand opinions, comments, and following their friends. Social networks are characterized by velocity, volume, value, variety, and veracity, the 5 V's of big data. Hence, big data analytic techniques and frameworks are commonly exploited in Social Network Analysis (SNA). By the ever-increasing growth of social networks, the analysis of social data, to describe and find communication patterns among users and understand their behaviors, has attracted much attention. In this paper, we demonstrate how big data analytics meets social media, and a comprehensive review is provided on big data analytic approaches in social networks to search published studies between 2013 and August 2020, with 74 identified papers. The findings of this paper are presented in terms of main journals/conferences, yearly distributions, and the distribution of studies among publishers. Furthermore, the big data analytic approaches are classified into two main categories: Content-oriented approaches and network-oriented approaches. The main ideas, evaluation parameters, tools, evaluation methods, advantages, and disadvantages are also discussed in detail. Finally, the open challenges and future directions that are worth further investigating are discussed.

Automated summary

Social Networking Services connect people worldwide through sharing contents, photos, and videos, with big data analytics techniques commonly exploited in Social Network Analysis. The research mainly focuses on big data analytic approaches in social networks, classified into content-oriented and network-oriented methods, discussing their advantages and disadvantages.