From Global Village to Identity Tribes: Context Collapse and the Darkest Timeline

Open Access Journal | ISSN: 2183-2439

Article | Open Access

From Global Village to Identity Tribes: Context Collapse and the Darkest Timeline

  • Marco Bastos School of Information and Communication Studies, University College Dublin, Ireland

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Abstract:  In this article we trace the development of two narratives describing social media that informed much of internet scholarship. One draws from McLuhan’s axiom positing that communication networks would bring forth a ‘global village,’ a deliberate contradiction in terms to foreground the seamless integration of villages into a global community. Social media would shrink the world and reshape it into a village by moving information instantaneously from any location at any time. By leveraging network technology, it would further increase the density of connections within and across social communities, thereby integrating geographic and cultural areas into a village stretching across the globe. The second narrative comprises a set of metaphors equally inspired by geography but emphasizing instead identity and tribalism as opposed to integration and cooperation. Both narratives are spatially inspired and foreground real-world consequences, either by supporting cooperation or by ripping apart the fabric of society. They nonetheless offer opposing accounts of communication networks: the first is centered on communication and collaboration, and the second highlights polarization and division. The article traces the theoretical and technological developments driving these competing narratives and argues that a digitally enabled global society may in fact reinforce intergroup boundaries and outgroup stereotyping typical of geographically situated communities.

Keywords:  context collapse; disinformation; geography; global village; internet studies; polarization

Published:   23 July 2021


© Marco Bastos. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (, which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.