JOURNAL ARTICLE

'Hey Siri, You're Dumb!': Investigating Blurting Instances of Voice-Based Assistants.

  • Published In: Interacting with Computers, 2023, v. 35, n. 6. P. 763 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Rijhwani, V; Edwards, C 3 of 3

Abstract

This article investigates the phenomenon of "blurting"—spontaneous, unedited verbal outbursts—during interactions with voice-based assistants (VBAs) such as Siri, Alexa, and Google Assistant, within the framework of human–machine communication (HMC). Based on an online survey of 185 participants, the study identifies five main reasons for blurting: personal communication difficulties, frustration with VBA capabilities, personal issues, general abusive language toward VBAs, and curiosity or task-related blurts. Findings reveal that blurting correlates positively with personality traits like extraversion and neuroticism, argument frames centered on identity, play, dominance, and utility, as well as verbal aggressiveness and psychological reactance, while also showing that blurting situations are associated with lower perceived personal benefits and higher situational apprehension. The research supports the Computers Are Social Actors (CASA) paradigm, indicating that users apply social heuristics to VBAs similarly to human interlocutors, but with unique motivations tied to HMC. The study highlights implications for designing more effective and user-sensitive VBAs and calls for further research on blurting across diverse technologies and cultural contexts.

Additional Information

  • Source:Interacting with Computers. 2023/11, Vol. 35, Issue 6, p763
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2023
  • ISSN:0953-5438
  • DOI:10.1093/iwc/iwad047
  • Accession Number:177467951
  • Copyright Statement:Copyright of Interacting with Computers is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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