Knowledge sharing discourse types used by key actors in online affinity spaces. Issue 9 (18th June 2021)

• Record Type: Journal Article
• Authors: Sharma, Priya; Li, Qiyuan; Land, Susan M.
• Is Part Of: Information and learning sciences. Volume 122:Issue 9/10(2021)
• Page Start: 671
• Page End: 687
• Publication Date: 2021-06-18
• Subjects: Discourse analysis -- Knowledge sharing -- Everyday learning -- Social network analysis -- Association rule mining -- Affinity spaces; Information science -- Periodicals; Library science -- Periodicals; Information theory in education -- Periodicals; Libraries and education -- Periodicals; 020
• DOI: 10.1108/ILS-09-2020-0211
• Languages: English
• ISSNs: 2398-5348

Abstract

Abstract : Purpose: The growth of online social network sites and their conceptualization as affinity spaces makes them well suited for exploring how individuals share knowledge and practices around specific interests or affinities. The purpose of this study is to extend what is known about highly active/key actors in online affinity spaces, especially the ways in which they sustain and contribute to knowledge sharing. Design/methodology/approach: This study analyzed 514 discussion posts gathered from an online affinity space on disease management. This study used a variety of methods to answer the research questions: the authors used discourse analyses to examine the conversations in the online affinity space, social network analyses to identify the structure of participation in the space and association rule mining and sentiment analysis to identify co-occurrence of discourse codes and sentiment of the discussions. Findings: The results indicate that the quality and type of discourse varies considerably between key and other actors. Key actors' discourse in the network serves to elaborate on and explain ideas and concepts, whereas other actors provide a more supportive role and engage primarily in storytelling. Originality/value: This work extends what is known about informal mentoring and the role of key actors within affinity spaces by identifying specific discourse types and types of knowledge sharing that are characteristic of key actors. Also, this study provides an example of the use of a combination of rule mining association and sentiment analysis to characterize the nature of the affinity space.