Self-disclosure, Privacy concerns and Social Capital benefits interaction in FB

Tzortzaki, Eleni; Kitsiou, Angeliki; Sideri, Maria; Gritzalis, Stefanos

doi:10.1145/3003733.3003781

Cited by 4 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Self-disclosure on SNSs has opened up many opportunities for individuals to indulge in capital-enhancing activities (Tzortzaki et al , 2016). It is associated with subjective well-being, improved connectedness, relationship gains, social capital and relief from loneliness.…”

Section: Discussionmentioning

confidence: 99%

“…A burgeoning body of research has been carried out to ascertain the determinants of self-disclosure (Walsh et al , 2020). The use of SNSs to disclose information is associated with capital-enhancing activities (Aharony, 2016; Tzortzaki et al , 2016). According to Hallam and Zanella (2017), self-disclosure is considered an integral part of enhancing social capital by the creation and maintenance of connections between users.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Khan

Ikram

Murtaza

et al. 2021

View full text Add to dashboard Cite

PurposeThis 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.Design/methodology/approachThis 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.FindingsThe 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 implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).

show abstract

Section: Discussionmentioning

confidence: 99%