Year: 2022
Type: conference-paper
Abstract: The success of Wikipedia and other user-generated content communities has been driven by the openness of recruiting volunteers globally, but this openness has also led to a persistent lack of trust in its content. Despite several attempts at developing trust indicators to help readers more quickly and accurately assess the quality of content, challenges remain for practical deployment to general consumers. In this work we identify and address three key challenges: empirically determining which metrics from prior and existing community approaches most impact reader trust; 2) validating indicator placements and designs that are both compact yet noticed by readers; and 3) demonstrating that such indicators can not only lower trust but also increase perceived trust in the system when appropriate. By addressing these, we aim to provide a foundation for future tools that can practically increase trust in user generated content and the sociotechnical systems that generate and maintain them.
Authors: Andrew Kuznetsov, Margeigh Novotny, Jessica Klein, Diego Saez-Trumper, Aniket Kittur
Institutions: Carnegie Mellon University, Wikimedia Foundation
Language: English
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4225080929