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Publication date: 17 January 2025

Maximilian Haug, Christian Maier, Heiko Gewald and Tim Weitzel

Social media communities contain like-minded members who disclose opinions about various topics that are important to them. These communities often function as echo chambers…

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Abstract

Purpose

Social media communities contain like-minded members who disclose opinions about various topics that are important to them. These communities often function as echo chambers, filter bubbles or separate spaces for users to share conforming opinions and discredit others deliberately. In extreme cases, they build their alternative reality with limited information that can lead to real-world action, as seen in the storming of the capitol. Therefore, we need to better understand the mechanisms of opinion disclosure in such communities.

Design/methodology/approach

We base our research on the spiral of silence theory to understand both trait-based and state-based fear of isolation as the mechanism that prevents opposing opinions in three scenarios focusing on topics dominating the mainstream US media landscape at that time: immigration, presidential election and COVID-19. We recruited 164 participants from an online research platform and analyzed the data using structural equation modeling.

Findings

Our results reveal empirical evidence that state-based fear of isolation prompts community members to express agreement and support for the community’s opinion, regardless of their views. We show that hot-button issues impose an even greater danger of establishing an environment in online communities that becomes an echo chamber of filter bubbles.

Originality/value

The spiral of silence theory provides a fine-grained understanding of the concept of fear of isolation, which was either used as a trait or as a state. Furthermore, we go beyond the initial hypotheses of the spiral of silence and show that within online communities, members stay silent and start to argue against their own opinions.

Details

Internet Research, vol. 35 no. 7
Type: Research Article
ISSN: 1066-2243

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