A turn to language: How interactional sociolinguistics informs the redesign of prompt:response chatbot turns. (October 2020)

• Record Type: Journal Article
• Authors: Dippold, Doris; Lynden, Jenny; Shrubsall, Rob; Ingram, Rich
• Is Part Of: Discourse, context & media. Volume 37(2020)
• Publication Date: 2020-10
• Subjects: Chatbots -- Micro-analysis -- Trust -- Alignment -- Affiliation -- Social presence; Discourse analysis -- Periodicals; Digital media -- Periodicals; Mass media and language -- Periodicals; Communication -- Periodicals; Communication; Digital media; Discourse analysis; Mass media and language; Periodicals; 401.4105
• DOI: 10.1016/j.dcm.2020.100432
• Languages: English
• ISSNs: 2211-6958

Abstract

Highlights: Trust and social presence and underpin user engagement with chatbots. Positivistic and interpretivist methodologies are inadequate to explain human:bot interaction. A microlevel linguistic approach is based on politeness theory, repair, epistemic stance and social presence. Microlevel analysis identifies problematic turn features leading to misalignment/disaffiliation. Redesign of prompt:response chatbot turns can improve user engagement and satisfaction. Abstract: This paper discusses how a microlevel linguistic analysis, using interactional sociolinguistics as an umbrella framework and drawing on analytical concepts from politeness theory and conversation analysis, can be used to advise chatbot designers on the interactional features contributing to problematic human user engagement as part of a consultancy project. Existing research using a microlevel linguistic analysis has analysed human user:bot interactions using natural language. This research has identified a central role for language which promotes sociability between the machine and users in the alignment of their goals and practices. However, there is no research currently which discusses how a microlevel linguistic analysis can help identify how the discursive construction of alignment and affiliation within prompt:response chatbots supports social presence and trust. This paper addresses this gap through an analysis of a database of prompt:response chatbot interactions which identified problematic sequences involving misalignment and disaffiliation, undermining human users' trust and sense of social presence within the interaction. It also reports on how the consultancy project suggested changes to the programming of the chatbot which have potential to lead to improved user engagement and satisfaction.