Next Article in Journal / Special Issue
Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine
Previous Article in Journal
Fractal Information by Means of Harmonic Mappings and Some Physical Implications
Previous Article in Special Issue
An Informed Framework for Training Classifiers from Social Media
Article Menu

Export Article

Open AccessArticle
Entropy 2016, 18(5), 164; doi:10.3390/e18050164

Finding Influential Users in Social Media Using Association Rule Learning

1
Blekinge Institute of Technology, Karlskrona 371 79, Sweden
2
Wrocƚaw University of Technology, 50-370 Wrocƚaw, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Holzinger
Received: 30 January 2016 / Revised: 12 April 2016 / Accepted: 22 April 2016 / Published: 27 April 2016
View Full-Text   |   Download PDF [396 KB, uploaded 28 April 2016]   |  

Abstract

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods. View Full-Text
Keywords: social media; data mining; association rule learning; prediction; social network analysis social media; data mining; association rule learning; prediction; social network analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Erlandsson, F.; Bródka, P.; Borg, A.; Johnson, H. Finding Influential Users in Social Media Using Association Rule Learning. Entropy 2016, 18, 164.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top