Estimating the self-esteem of social network users based on their account information

Articles in Press

Document Type : Original Article

Authors

1 Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

2 Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran,

Abstract

The users’ interactions in the social networks can reflect their personality traits. Therefore, by analyzing the data of users' interactions, their personality traits can be estimated. Then, the estimated personality traits can be used for various purposes, including monitoring the mental health of users, personalizing content and services to the users, marketing and advertising activities. In this study, the focus is on the self-esteem personality trait, and models are presented to estimate the self-esteem of Iranian users of the Instagram social network. For this purpose, first a population of Instagram users was considered. These users completed the Rosenberg questionnaire and their self-esteem was measured. Then, various features (e.g., the number of followers and followers of users, and their profile image specifications) were also extracted from the account of these users to be used together with their self-esteem level. By applying machine learning algorithms on these data, models were extracted to estimate users' confidence. The evaluation shows that the proposed method can estimate users' self-esteem based on the information of users’ Instagram account with accuracy of 0.81 and precision of 0.77. Also, the results showed that if the self-esteem of males and females is modeled separately, more accurate models will be obtained.

Keywords

Main Subjects

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 11 August 2024

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History

  • Receive Date: 30 December 2023
  • Revise Date: 05 May 2024
  • Accept Date: 12 July 2024

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