Social media users and cybersecurity awareness: predicting self-disclosure using a hybrid artificial intelligence approach

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.

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.

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.

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.

This study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).