A novel approach for efficient stance detection in online social networks with metaheuristic optimization. (February 2021)

• Record Type: Journal Article
• Authors: Can, Umit; Alatas, Bilal
• Is Part Of: Technology in society. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: Stance detection -- Metaheuristic optimization -- Online social network problems -- Online social network analysis -- Data mining; BBBC Big Bang Big Crunch -- GWO Grey Wolf Optimization Algorithm -- HWO-BBBC Hybrid Whale Optimization-Big Bang Big Crunch Algorithm -- NER Named entity recognition -- OSN Online social network -- SVM Support Vector Machines -- WOA Whale Optimization Algorithm; Technology -- Social aspects -- Periodicals; 303.483
• DOI: 10.1016/j.techsoc.2020.101501
• Languages: English
• ISSNs: 0160-791X

Abstract

Abstract: In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods. Highlights: A novel method for solving the problem of Stance detection has been proposed. Stance detection problem has been handled as an optimization problem for the first time. A new area has been addressed for optimization. An efficient novel hybrid optimization algorithm has been developed.