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.

Mitigating Academic Institution Dropout Rates with Predictive Analytics Algorithms

“…The differential model in Figure 5 below illustrates the percentage of most common models used worldwide compared with their popularity in NA. (Nanayakkara et al, 2021;Lough, 2022;NCES, 2021) From figure 5 above, Bayesian Networks (BN) and Artificial Neural networks (ANN) were the most applied models between 2017 and 2020 for constructing PAAs in NA and worldwide (Lough, 2022). The graph also shows the popularity of adopting k-nearest Neighbors (k-NN) and Decision Tree (DT) (NCES, 2021).…”

“…In contrast, the Naive Bayes Model (NBM) and Apriori Algorithm have been marginally used (Nanayakkara et al, 2021).…”

“…Figure 3: Models used in NA versus globally Sources:(Nanayakkara et al, 2021;Lough, 2022;NCES, 2021) …”

Thai Commercial Banks on Twitter

“…For banks, big data analytics provides various advantages for banks and their customers, including fraud detection and prevention, customer segmentation, risk management, and future predictions (More & Moily, 2021). SM analytics with data-mining techniques could be applied to extract trends, patterns, and rules from the SM pool (Nanayakkara et al, 2021). In the banking field, data mining is used to detect fraud, assess risks, and analyze trends and profitability (Nanayakkara et al, 2021).…”

A Review on Current Trends and Applications of Social Media Research in Sri Lanka

“…Several literature reviews exist on social media applications, analytics, and their effects concerning various fields. Examples include systematic literature reviews that discuss techniques, software tools and platforms for social media analytics [1][2][5][6] and review articles that focus on domains such as education [7][8], health [9], politics [10], disaster management [11] and business and innovation [12]. Although there are several literature reviews on the data and usage of social media in different application fields, we could not locate any that discussed the general trends and applications of research on social media usage concerning a specific country.…”