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- Publisher
- Springer Journals
- Copyright
- Copyright © 2016 by Springer-Verlag Wien
- Subject
- Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Methodology of the Social Sciences
- ISSN
- 1869-5450
- eISSN
- 1869-5469
- DOI
- 10.1007/s13278-016-0412-3
- Publisher site
- See Article on Publisher Site
Abstract
Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender and age; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots versus humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights into researchers and better services to online users.
Journal
Social Network Analysis and Mining – Springer Journals
Published: Nov 4, 2016