finding influential users in social media using association rule learning

Clicks: 213
ID: 229318
2016
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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.
Reference Key
erlandsson2016entropyfinding Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Fredrik Erlandsson;Piotr Bródka;Anton Borg;Henric Johnson
Journal European journal of medicinal chemistry
Year 2016
DOI
10.3390/e18050164
URL
http://www.mdpi.com/1099-4300/18/5/164
Keywords
Data mining social media prediction association rule learning

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