Social Media Types: introducing a data driven taxonomy

Social Media (SM) have been established as multifunctional networking tools that tend to offer an increasingly wider variety of services, making it difficult to determine their core purpose and mission, therefore, their type. This paper assesses this evolution of Social Media Types (SMTs), presents, and evaluates a novel hypothesis-based data driven methodology for analyzing Social Media Platforms (SMPs) and categorizing SMTs. We review and update literature regarding the categorization of SMPs, based on their services. We develop a methodology to propose and evaluate a new taxonomy, comprising: (i) the hypothesis that the number of SMTs is smaller than what current literature suggests, (ii) observations on data regarding SM usage and (iii) experimentation using association rules and clustering algorithms. As a result, we propose three (3) SMTs, namely Social, Entertainment and Profiling networks, typically capturing emerging SMP services. Our results show that our hypothesis is validated by implementing our methodology and we discuss threats to validity.

[1]  Peter J. Rousseeuw,et al.  Clustering by means of medoids , 1987 .

[2]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[3]  Masaaki Sibuya,et al.  A random clustering process , 1993 .

[4]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[5]  S. Wasserman,et al.  Social Network Analysis: List of Illustrations , 1994 .

[6]  Sholom M. Weiss,et al.  Rule-based Machine Learning Methods for Functional Prediction , 1995, J. Artif. Intell. Res..

[7]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[8]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[11]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[12]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[13]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[14]  Ingo Mierswa,et al.  YALE: Yet Another Learning Environment , 2003 .

[15]  Kurt Hornik,et al.  A CLUE for CLUster Ensembles , 2005 .

[16]  Kurt Hornik,et al.  Introduction to arules – A computational environment for mining association rules and frequent item sets , 2009 .

[17]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[18]  Danah Boyd,et al.  Social network sites: definition, history, and scholarship , 2007, IEEE Engineering Management Review.

[19]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[20]  B. Santhosh Kumar,et al.  Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms , 2010 .

[21]  C. Tappert,et al.  A Survey of Binary Similarity and Distance Measures , 2010 .

[22]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[23]  Nikos Tsirakis,et al.  k-ATTRACTORS: A PARTITIONAL CLUSTERING ALGORITHM FOR NUMERIC DATA ANALYSIS , 2011, Appl. Artif. Intell..

[24]  Marios D. Dikaiakos,et al.  Online Social Networks: Status and Trends , 2011, New Directions in Web Data Management 1.

[25]  Bruno S. Silvestre,et al.  Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media , 2011 .

[26]  Jaume Bacardit,et al.  Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets[C][W][OA] , 2011, Plant Cell.

[27]  Lakhmi C. Jain,et al.  New Directions in Web Data Management 1 , 2011, New Directions in Web Data Management 1.

[28]  Bernardo A. Huberman,et al.  Trends in Social Media: Persistence and Decay , 2011, ICWSM.

[29]  Huan Liu,et al.  Mining Social Media: A Brief Introduction , 2012 .

[30]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[31]  Michael Netter,et al.  Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control , 2013, 2013 International Conference on Availability, Reliability and Security.

[32]  Reza Zafarani,et al.  Social Media Mining: An Introduction , 2014 .

[33]  Jonathan A. Obar,et al.  Social Media Definition and the Governance Challenge: An Introduction to the Special Issue , 2015 .

[34]  Jin Gerlach,et al.  Understanding the Anatomy of Data-Driven Business Models - towards an Empirical Taxonomy , 2016, ECIS.

[35]  Mahdi Fazeli,et al.  ARMICA-Improved: A New Approach for Association Rule Mining , 2017, KSEM.

[36]  Hans-Peter Kriegel,et al.  DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..

[37]  Hans-Bernd Brosius,et al.  Functional domains of social media platforms: Structuring the uses of Facebook to better understand its gratifications , 2018, Comput. Hum. Behav..