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Will online digital footprints reveal your relationship status? an empirical study of social applications for sexual-minority men

Research output: Contribution to journalJournal articlepeer-review

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  • J. Wang
  • J. Ma
  • Y. Wang
  • N. Wang
  • L. Wang
  • D. Zhang
  • F. Wang
  • Q. Lv
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Article number29
<mark>Journal publication date</mark>1/03/2020
<mark>Journal</mark>Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number1
Volume4
Number of pages23
Publication StatusPublished
<mark>Original language</mark>English

Abstract

With the increasing social acceptance and openness, more and more sexual-minority men (SMM) have succeeded in creating and sustaining steady relationships in recent years. Maintaining steady relationships is beneficial to the wellbeing of SMM both mentally and physically. However, the relationship maintaining for them is also challenging due to the much less supports compared to the heterosexual couples, so that it is important to identify those SMM in steady relationship and provide corresponding personalized assistance. Furthermore, knowing SMM's relationship and the correlations with other visible features is also beneficial for optimizing the social applications' functionalities in terms of privacy preserving and friends recommendation. With the prevalence of SMM-oriented social apps (called SMMSA for short), this paper investigates the relationship status of SMM from a new perspective, that is, by introducing the SMM's online digital footprints left on SMMSA (e.g., presented profile, social interactions, expressions, sentiment, and mobility trajectories). Specifically, using a filtered dataset containing 2,359 active SMMSA users with their self-reported relationship status and publicly available app usage data, we explore the correlations between SMM's relationship status and their online digital footprints on SMMSA and present a set of interesting findings. Moreover, we demonstrate that by utilizing such correlations, it has the potential to construct machine-learning-based models for relationship status inference. Finally, we elaborate on the implications of our findings from the perspective of better understanding the SMM community and improving their social welfare. © 2020 Association for Computing Machinery.