Abstract:
Social networks have gained exceptional attention within the last decade. Social
network sites like Twitter, Facebook, YouTube, and LinkedIn are evolving in a
speckled fashion. Users rely on social networks for both information and
entertainment needs. Social media analytics with data mining technology could be
an analysis axis cantered 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 between 2010 and 2018. The study demonstrates a systematic review of
analysing trends and content analysis of studies within the field of social media
analytics that were published in databases principally IEEE, Elsevier, ScienceDirect,
and ResearchGate. Hundred 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. Data mining techniques were utilized
for retrieval of information, statistical modelling, and machine learning that engage
data pre-processing, data analysis, and data interpretation. The review discovered
that fifteen data mining techniques were employed in social media data while
frequently used in Support Vector Machine, Bayesian networks, and Decision Tree.
The study focused on assisting the involved analysers and educators to capture the
research trends and problems associated with the Social media analytics process with
future research initiatives
