Performing the Digital Self: Understanding Location-Based Social Networking, Territory, Space, and Identity in the City
Expressions of territoriality have been positioned as one of the main reasons users alter their behaviors and perceptions of spatiality and sociality while engaging with location-based social networks (LBSN). Despite the potential for this interplay to further our understanding of LBSN usage in the context of identity, very little work has actually been done towards this. Addressing this gap in the literature is one the chief aims of the article. Drawing on an original six-week study with 42 participants utilizing a bespoke LBSN entitled ‘GeoMoments’, our research explores: (1) the way that territoriality is linked to self-identity; and (2) how this interplay affects the interactions between users as well as the environments they inhabit. Our findings suggest that participants affirmed their self-identity by selectively posting and claiming ownership of their neighborhood through the LBSN. Here, the locative decisions made related to risk, hierarchies, and the users’ relationship to the area. This practice then led participants to discover and interact with the digital information overlaying their physical environments in a playful manner. These interactions demonstrate the perceived power structures that are facilitated by identity claims over a virtual area. In the main, our results reaffirm that territoriality is a central concept in understanding LBSN use, while also drawing attention to the temporality involved in user-to-user and user-to-place interactions pertaining to physical place mediated by LBSN.
Department, Program, or Center
School of Interactive Games and Media (GCCIS)
Konstantinos Papangelis, Alan Chamberlain, Ioanna Lykourentzou, Vassilis-Javed Khan, Michael Saker, Hai-Ning Liang, Irwyn Sadien, and Ting Cao. 2020. Performing the Digital Self: Understanding Location-Based Social Networking, Territory, Space, and Identity in the City. ACM Trans. Comput.-Hum. Interact. 27, 1, Article 1 (January 2020), 26 pages. DOI:https://doi.org/10.1145/3364997
RIT – Main Campus
© ACM 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Computer-Human Interaction (TOCHI), https://doi.org/10.1145/3364997