Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00607-019-00739-y | Computing |
Keywords | Field | DocType |
Social Media, Social Media Types, Social Media Sites, Social Media Platforms, Social networking, Data mining, Clustering, Association rules, 68U35 | Data science,Categorization,Mathematical optimization,Social network,Social media,Data-driven,Profiling (computer programming),Entertainment,Association rule learning,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
102 | 1 | 0010-485X |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paraskevas Koukaras | 1 | 3 | 1.06 |
| Christos Tjortjis | 2 | 173 | 24.40 |
| Dimitrios Rousidis | 3 | 3 | 1.39 |