ECharacterize: A Novel Feature Selection-Based Framework for Characterizing Entrepreneurial Influencers in Arabic Twitter

Authors

  • Bodor Moheel Almotairy King Abdulaziz University
  • Manal Abdullah King Abdulaziz University
  • Rabeeh Abbasi Quaid-i-Azam University

DOI:

https://doi.org/10.3991/ijim.v14i10.14807

Keywords:

Twitter, characteristics of influencers, entrepreneurial influencers, robust ranked list.

Abstract


Abstract— Social media are widely used as communication platforms in the world of business. Twitter, in particular, offers valuable opportunities for collaboration due to its open nature. For that, many entrepreneurs employ Twitter for different reasons, such as mobilizing financial resources, get funding, and increase their innovation capabilities. Therefore, they keep looking for local entrepreneurial accounts to help them. Messages from entrepreneurial influencers -opinion leader- increase the information diffusion to entrepreneurs, helping them to find more opportunities. Discovering the characteristics of entrepreneurial influencers in Twitter networks becomes extremely important since it reflects the way to reach entrepreneurs. In the present paper, we propose a novel framework called ECharacterize based on feature selections techniques to discover the characteristics of the entrepreneurial influencer in the Saudi context in a robust manner. The framework extracts abundant influencers’ features and then employs seven state-of-the-art ranking methods to determine the characteristics of the most relevant influencer. It robustly aggregates the lists to come out with the accurate final list using Robust Rank Aggregation. The framework examined on 233,018 real-life Arabic tweets. The results show the ability of the proposed method to distinguish between the influencers by their popularity, reliability and activity level.   

Author Biographies

Bodor Moheel Almotairy, King Abdulaziz University

B.Moheel Almotairy completed her master’s degree in information system Department at the Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia in 2020. She obtained her bachelor’s degree with first honor from King Abdulaziz University. Her research field’s interest includes Data Science and Social Network Analysis.

Manal Abdullah, King Abdulaziz University

M.Abdulaziz Abdullah. received her PhD in computers and systems engineering, Faculty of engineering, Ain Shams University, Cairo, Egypt, 2002. She has experienced in industrial computer networks and embedded systems. Her research interests include Artificial Intelligence, performance evaluation, WSN, network management, Big Data analysis, and pattern recognition. Dr Abdullah published more than 120 research papers in various international journals and conferences. She has also joined many HiCi research projects all over the world.

Rabeeh Abbasi, Quaid-i-Azam University

R.Abbasi completed his PhD from University of Koblenz-Landau, Germany in 2010. He is working as an associate professor at the Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan. He has a vast research experience in the fields of social media analytics and social network analysis. His research focuses on leveraging positive aspects of social media including social media's use in saving lives, understanding events, and analyzing sentiments among many others. He has published more than 35 articles in reputed journals like IEEE Computational Intelligence Magazine, Computers in Human Behavior, Telematics and Informatics, Applied Soft Computing, and Scientometrics and international conferences like ACM HyperText Conference, ACM World Wide Web Conference, Pacific Asia Conference on Knowledge Discovery and Data mining, and European Conference on Information Retrieval. 

 

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Published

2020-06-30

How to Cite

Almotairy, B. M., Abdullah, M., & Abbasi, R. (2020). ECharacterize: A Novel Feature Selection-Based Framework for Characterizing Entrepreneurial Influencers in Arabic Twitter. International Journal of Interactive Mobile Technologies (iJIM), 14(10), pp. 74–89. https://doi.org/10.3991/ijim.v14i10.14807

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Section

Papers