A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Abstract

Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.