Exploring the Grassroots Understanding and Practices of Collective Memory Co-Contribution in a University Community

Collective memory—community members' interconnected memories and impressions of the group—is essential to the community's culture and identity. Its development requires members' continuous participatory contribution and sensemaking. However, existing works mainly adopt a holistic sociological perspective to analyze well-developed collective memory, less focusing on member-level conceptualization of this possession or what the co-contribution practices can be. Therefore, this work alternatively adopts the latter perspective and probes such interpretative and interactional patterns with two mobile systems. With one being a locative narrative and exploration system condensed from existing literature's design frameworks, and the other being a conventional online forum representing current practices, they served as the anchors of observation for our two-week, mixed-methods field study (n=38) on a university campus. A core debate we have identified was to retrospectively contemplate or document the presence as a history for the future. This also subsequently impacted the narrative focuses, expectations of collective memory constituents, and the ways participants seek inspiration from the group. We further extracted design considerations that could better embrace the diverse conceptualizations of collective memory and bond different community members together. Lastly, revisiting and reflecting on our design, we provided extra insights on designing devoted locative narrative experiences for community-driven UGC platforms.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2026-03-31
DOI
https://doi.org/10.1145/3799444
Primary Topic
Innovative Human-Technology Interaction
Type
article
Field-Weighted Citation Impact
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article

Exploring the Grassroots Understanding and Practices of Collective Memory Co-Contribution in a University Community

Junze Li, Zeyu Huang, Xinyi Cao, Kangyu Yuan et al.
Proceedings of the ACM on Human-Computer Interaction
Innovative Human-Technology Interaction
article

Exploring the Grassroots Understanding and Practices of Collective Memory Co-Contribution in a University Community

Junze Li, Zeyu Huang, Xinyi Cao, Kangyu Yuan, Xiaojuan Ma
article en

Abstract

Collective memory—community members' interconnected memories and impressions of the group—is essential to the community's culture and identity. Its development requires members' continuous participatory contribution and sensemaking. However, existing works mainly adopt a holistic sociological perspective to analyze well-developed collective memory, less focusing on member-level conceptualization of this possession or what the co-contribution practices can be. Therefore, this work alternatively adopts the latter perspective and probes such interpretative and interactional patterns with two mobile systems. With one being a locative narrative and exploration system condensed from existing literature's design frameworks, and the other being a conventional online forum representing current practices, they served as the anchors of observation for our two-week, mixed-methods field study (n=38) on a university campus. A core debate we have identified was to retrospectively contemplate or document the presence as a history for the future. This also subsequently impacted the narrative focuses, expectations of collective memory constituents, and the ways participants seek inspiration from the group. We further extracted design considerations that could better embrace the diverse conceptualizations of collective memory and bond different community members together. Lastly, revisiting and reflecting on our design, we provided extra insights on designing devoted locative narrative experiences for community-driven UGC platforms.

Proceedings of the ACM on Human-Computer InteractionVol. 10(1)
Hong Kong University of Science and Technology (HK), Hong Kong Science and Technology Parks Corporation (HK), University of Hong Kong (HK), Stanford University (US)
Reduced inequalities
Openalex Percentile: Top 32%
Innovative Human-Technology Interaction
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