Paper Number
ICIS2025-2483
Paper Type
Short
Abstract
Filter bubbles describe online environments in which users are exposed primarily to homogeneous content while shielded from diverse perspectives. Although prior research has examined causes and consequences, the underlying processes remain conceptually unclear. To address this gap, we adopt a two-step approach. First, based on a systematic literature review, we applied a grounded theory method to identify sub-attributes and aggregate them into attributes of filter bubbles. Second, we employed the socio-technical perspective to classify these attributes, underscoring that filter bubbles emerge only through the interplay of social and technical systems. Our preliminary findings highlight attributes such as social media platform providers (structure), mental models (people), personalized filtering algorithms (technology), and personalization (task). We expect to contribute to literature by conceptually delineating the processes within filter bubbles and providing a scaffold for theorizing their interactions, thereby laying a foundation for future research.
Recommended Citation
Gimnich, Moritz; Weinert, Christoph; and Weitzel, Tim, "Deconstructing Filter Bubbles: Attributes and Interactions" (2025). ICIS 2025 Proceedings. 13.
https://aisel.aisnet.org/icis2025/is_media/is_media/13
Deconstructing Filter Bubbles: Attributes and Interactions
Filter bubbles describe online environments in which users are exposed primarily to homogeneous content while shielded from diverse perspectives. Although prior research has examined causes and consequences, the underlying processes remain conceptually unclear. To address this gap, we adopt a two-step approach. First, based on a systematic literature review, we applied a grounded theory method to identify sub-attributes and aggregate them into attributes of filter bubbles. Second, we employed the socio-technical perspective to classify these attributes, underscoring that filter bubbles emerge only through the interplay of social and technical systems. Our preliminary findings highlight attributes such as social media platform providers (structure), mental models (people), personalized filtering algorithms (technology), and personalization (task). We expect to contribute to literature by conceptually delineating the processes within filter bubbles and providing a scaffold for theorizing their interactions, thereby laying a foundation for future research.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
23-Media