Open Access
ARTICLE
Finding Temporal Influential Users in Social Media Using Association Rule Learning
Babar Shazad1, Hikmat Ullah khan2, Zahoor-ur-Rehman1, Muhammad Farooq2, Ahsan Mahmood1, Irfan Mehmood3,*, Seungmin Rho3, Yunyoung Nam4,*
1 Dept. of Computer Science COMSATS University Islamabad, Attock, Pakistan
2 Dept. of Computer Science COMSATS University Islamabad, Wah Pakistan
3 Department of Software, Sejong University, Seoul, Korea
4 Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
* Corresponding Authors: Irfan Mehmood, Yunyoung Nam, ; ,
Intelligent Automation & Soft Computing 2020, 26(1), 87-98. https://doi.org/10.31209/2019.100000130
Abstract
The social media has become an integral part of our daily life. The social web
users interact and thus influence each other influence in many aspects.
Blogging is one of the most important features of the social web. The bloggers
share their views, opinions and ideas in the form of blog posts. The influential
bloggers are the leading bloggers who influence the other bloggers in their
online communities. The relevant literature presents several studies related to
identification of top influential bloggers in last decade. The research domain of
finding the top influential bloggers mainly focuses on feature centric models.
This research study proposes to apply association rule learning for finding the
temporal influential bloggers. The widely used Apriori algorithm is applied using
Oracle data miner to find the frequent pattern of bloggers having blog activities
together and then we find who influences others based on the rules learned
from the association rule mining. The use of standard evaluation measures such
as accuracy, precision and F1 score verifies the results. This research study
uses the standard dataset of TechCrunch which is a real world blog. The results
confirm that the association rule mining can produce rules which help to find
the temporal influential bloggers in the blogosphere who are consistent on
regular basis. The proposed method achieved accuracy as high as 98% for
confidence level of 90%. The identification of the top influential bloggers has
enormous applications in advertising, online marketing, e-commerce, promoting
a political agenda, influencing elections and affect the government policies.
Keywords
Cite This Article
B. Shazad, H. U. Khan, . Zahoor-ur-Rehman, M. Farooq, A. Mahmood
et al., "Finding temporal influential users in social media using association rule learning,"
Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 87–98, 2020. https://doi.org/10.31209/2019.100000130