Social Media Data Mining Techniques: A Survey

Tiwari, Dimple; Kumar, Manoj

doi:10.1007/978-981-13-7166-0_18

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…Clustering algorithms do not depend on labeled training data to generate a model, unlike classification algorithms. As an alternative, clustering algorithms determine which elements in the data set are similar to each other based on the similarity of the data items (Tiwari & Kumar, 2020).…”

Section: Data Mining In Telecommunicationmentioning

confidence: 99%

“…This situation has staged the presence of 'Data Mining' (Pal, 2011). Data mining is the process of digging meaningful insights from large data sets by using various statistical, machine learning, databases, and Artificial Intelligence methods and dispersing the knowledge into a form that is advantageous for various real-world applications (Tiwari & Kumar, 2020). It facilitates predicting future trends and behaviors, making proactive decisions, increasing profits, and cut costs, to answer business questions that consume too much time to answer (Fawzy et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Facebook, Twitter, LinkedIn, YouTube, etc. (Tiwari & Kumar, 2020). This has improved the concept and the technology of Web 2.0, by enabling the formation and exchange of User-Generated Content (Kaplan & Haenlein, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Different data mining techniques have been studied to process and analyze several types of data patterns, where the most popular data mining tasks were classification, summarization, association rules mining, and clustering in the selected period. (Tiwari & Kumar, 2020). The problem statement which stimulates the study is "How were the data mining techniques being evolved concerning the social media research during the last decade?"…”

Section: Introductionmentioning

confidence: 99%

“…The decision of selecting an approach whether supervised or unsupervised is depended on the data set and the problem domain. Classification is a common supervised approach and is appropriate when the data set has labels or a small portion of the data has labels(Tiwari & Kumar, 2020).…”

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confidence: 99%

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

Nanayakkara

Kumara

Rathnayaka

2021

SL J. Soc. Sci. Hum.

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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.

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Section: Data Mining In Telecommunicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%