Identifying topical influencers on twitter based on user behavior and network topology

Alp Z. Z., Öğüdücü Ş.

KNOWLEDGE-BASED SYSTEMS, vol.141, pp.211-221, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 141
  • Publication Date: 2018
  • Doi Number: 10.1016/j.knosys.2017.11.021
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.211-221
  • Istanbul Technical University Affiliated: Yes


Social media web sites have become major media platforms to share personal information, news, photos, videos and more. Users can even share live streams whenever they want to reach out to many other. This prevalent usage of social media attracted companies, data scientists, and researchers who are trying to infer meaningful information from this vast amount of data. Information diffusion and maximizing the spread of words is one of the most important focus for researchers working on social media. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. In this research, finding topical influential/authority users on Twitter is addressed. Since Twitter is a good platform to spread knowledge as a word of mouth approach and it has many more public profiles than protected ones, it is a target media for marketers. In this paper, we introduce a novel methodology, called Personalized PageRank, that integrates both the information obtained from network topology and the information obtained from user actions and activities in Twitter. The proposed approach aims to determine the topical influencers who are experts on a specific topic. Experimental results on a large dataset consisting of Turkish tweets show that using user specific features like topical focus rate, activeness, authenticity and speed of getting reaction on specific topics positively affects identifying influencers and lead to higher information diffusion. Algorithms are implemented on a distributed computing environment which makes high-cost graph processing more efficient. (C) 2017 Elsevier B.V. All rights reserved.