Exploring the collective knowledge curation process of online health communities

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] More and more people turn to online health communities for social support to satisfy their health-related needs. Previous studies on social support and online health communities in general have focused on the content of social support and the relationship of social support with other entities using traditional social science methods. Little is known about how social support facilitate the knowledge curation process in an online health community. Moreover, the presence of misinformation in online health communities also calls for research into the knowledge curation process in order to reduce the risk of misinformation. This study uses data mining technologies to analyze around one million posts across 23 online health communities. It aims to reveal how information, through social support, flows between the community users working as a whole to dynamically curate knowledge and further interacts with information accuracy. This data-centric research in online health communities 1) discovered that xperiphery users instead of core users dominate the quantitative and content information flow; 2) identified three temporal information flow patterns for the knowledge curation process -- each with distinct characteristics; 3) found that information accuracy differed significantly over the identified information flow patterns and time and the information accuracy variation trends with each information flow pattern was identified as well. These findings not only have important implications for social support use, delivery and social support research methodologies but also can inform future online health platform design.