Abstract:
The Social Web Gendered Privacy Model consists of four key components (physical security, harassment, social communication needs and social communication skills) which intend to explain privacy-related gender differences in the Social Web. A core assumption of the model is that gendered communication patterns and concerns are often transferred from the offline realm to the online realm. However, the model’s applicability to inherently unique social network sites as well as females belonging to distinctive demographic segments has not yet been assessed. Thus, testing the model’s validity within a wide range of contexts proves to be necessary in understanding its relevance and robustness in a modern technologically-mediated environment. This research qualitatively assesses the model from the perspective of 22 late-adolescent females whom use Facebook, Instagram and/or Snapchat. The results conclude that late-adolescent females use social network sites in a unique and complex manner. The norms associated with the different platforms in combination with the developmental period of adolescence itself has resulted in a lack of privacy-protective actions and voluntary exposure to comparatively high-risk scenarios. These results are inconsistent with the predictions made by the model, indicating that the model is not generalizable to all demographic segments and social network sites. While this research may be limited by its qualitative nature and focus on Western societies, its findings necessitate a variety of future research. Firstly, the study proposes that the model’s generalisability is restricted. Quantitative research carried out with a representative sample is required in order to reinforce the claims made by this study and correct the overall model. Secondly, as the model is based on seminal literature, invalidating the predictions of the model may limit the contemporary relevance of preceding research. Investigating research with strong assumptions about females and adolescents, especially in the context of the Internet or social network sites, is likely to yield interesting results.