Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search
CoRR(2024)
Abstract
Instruments such as eye-tracking devices have contributed to understanding
how users interact with screen-based search engines. However, user-system
interactions in audio-only channels – as is the case for Spoken Conversational
Search (SCS) – are harder to characterize, given the lack of instruments to
effectively and precisely capture interactions. Furthermore, in this era of
information overload, cognitive bias can significantly impact how we seek and
consume information – especially in the context of controversial topics or
multiple viewpoints. This paper draws upon insights from multiple disciplines
(including information seeking, psychology, cognitive science, and wearable
sensors) to provoke novel conversations in the community. To this end, we
discuss future opportunities and propose a framework including multimodal
instruments and methods for experimental designs and settings. We demonstrate
preliminary results as an example. We also outline the challenges and offer
suggestions for adopting this multimodal approach, including ethical
considerations, to assist future researchers and practitioners in exploring
cognitive biases in SCS.
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