The perception of humanness in conversational journalism: An algorithmic information-processing perspective
Source of Publication
New Media and Society
How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ.
Computer Sciences | Social and Behavioral Sciences
algorithmic information processing, anthropomorphized chatbots, conversational journalism, explanatory cues, perceived humanness, social cues in AI, two-step flow
Shin, Donghee, "The perception of humanness in conversational journalism: An algorithmic information-processing perspective" (2021). All Works. 4091.
Indexed in Scopus