Taxonomy of Community Detection over Social Media
Smita Shende1, Divakar Singh2

1Smita Shende, Research Scholar, Department of Computer Science and Engineering, Barkatullah University Institute of Technology (BUIT), Bhopal, M.P., India.
2Dr. Divakar Singh, Assistant Professor, Department of Computer Science and Engineering, Barkatullah University Institute of Technology (BUIT), Bhopal, M.P., India.
Manuscript received on 21 August 2019. | Revised Manuscript received on 16 September 2019. | Manuscript published on 30 September 2019. | PP: 758-764 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14470981119/2019©BEIESP | DOI: 10.35940/ijitee.K1447.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Advancements in web technologies in conjunction with the advent of social media facilitate online users to share contents and interact on a shared platform. Social media mining allows users to visualize, evaluate, analyze, and extract meaningful patterns and trends over the social network. Numerous methods and algorithms have been presented for the massive investigation of social media data. Community detection over social media is the most attracting field of interest for researchers in the area of social media mining. Community detection is a process of identifying densely connected network nodes and forming a group or community based on the density of interconnection among them. Detection of such communities is very crucial for a variety of applications in order to analyze the social network. This paper provides a brief introduction of social media, social media mining, and highlights prominent and recent research works done in the field of community detection. The paper presents the taxonomy of various algorithms and approaches for community detection over social media. The paper also includes in-depth details of extent community detection methods devised in the literature to detect communities over social media.
Keywords: Community detection, Community structure Social media, Social media mining, Social network, Survey.
Scope of the Article: