A Turing Test for ''Localness'': Conceptualizing, Defining, and Recognizing Localness in People and Machines

TL;DR

This paper explores how humans and AI recognize localness in digital interactions, proposing a multi-dimensional framework and identifying cues that influence perceptions of local presence in place-based digital platforms.

Contribution

It introduces a novel Turing-like test for localness, combining philosophical insights with empirical analysis to understand recognition cues and improve location-based service design.

Findings

People are more accurate at recognizing locals than nonlocals.

Localness is an active status requiring demonstration, not just absence of nonlocal traits.

Artificial agents can be perceived as local under certain conditions.

Abstract

As digital platforms increasingly mediate interactions tied to place, ensuring genuine local participation is essential for maintaining trust and credibility in location-based services, community-driven platforms, and civic engagement systems. However, localness is a social and relational identity shaped by knowledge, participation, and community recognition. Drawing on the German philosopher Heidegger's concept of dwelling -- which extends beyond physical presence to encompass meaningful connection to place -- we investigate how people conceptualize and evaluate localness in both human and artificial agents. Using a chat-based interaction paradigm inspired by Turing's Imitation Game and Von Ahn's Games With A Purpose, we engaged 230 participants in conversations designed to examine the cues people rely on to assess local presence. Our findings reveal a multi-dimensional framework of localness, highlighting differences in how locals and nonlocals emphasize various aspects of local identity. We show that people are significantly more accurate in recognizing locals than nonlocals, suggesting that localness is an affirmative status requiring active demonstration rather than merely the absence of nonlocal traits. Additionally, we identify conditions under which artificial agents are perceived as local and analyze participants' sensemaking strategies in evaluating localness. Through predictive modeling, we determine key factors that drive accurate localness judgments. By bridging theoretical perspectives on human-place relationships with practical challenges in digital environments, our work informs the design of location-based services that foster meaningful local engagement. Our findings contribute to a broader understanding of localness as a dynamic and relational construct, reinforcing the importance of dwelling as a process of belonging, recognition, and engagement with place.