@article{ref1,
title="Social media users and cybersecurity awareness: predicting self-disclosure using a hybrid artificial intelligence approach",
journal="Kybernetes",
year="2023",
author="Khan, Naurin Farooq and Ikram, Naveed and Murtaza, Hajra and Asadi, Muhammad Aslam",
volume="52",
number="1",
pages="401-421",
abstract="PURPOSE This study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information. <br><br>DESIGN/METHODOLOGY/APPROACH This study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach. <br><br>FINDINGS The results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70. Practical implications The findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs. <br><br>ORIGINALITY/VALUE This study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).<p /> <p>Language: en</p>",
language="en",
issn="0368-492X",
doi="10.1108/K-05-2021-0377",
url="http://dx.doi.org/10.1108/K-05-2021-0377"
}