Social talk: making conversation with people and machine
Abstract
Social or interactive talk differs from task-based or instrumental interactions in many ways. Quantitative knowledge of these differences will aid the design of convincing human-machine interfaces for applications requiring machines to take on roles including social companions, healthcare providers, or tutors. We briefly review accounts of social talk from the literature. We outline a three part data collection of human-human, human-woz and human-machine dialogs incorporating light social talk and a guessing game. We finally describe our ongoing experiments on the corpus collected.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The adoption of human dialogue as a metaphor can be attributed to two factors: speech interfaces are increasingly designed and marketed to emulate human speech, and speech alone as an interaction modality may inherently provide a false affordance for possible interactions.
“which can attributed to two factors: 1) speech interfaces are increasingly designed and marketed to emulate the nuances of human speech [19, 21] and 2) speech alone as interaction modality may inherently provide a false affordance for possible interactions”
From Situated Understanding of Errors in Older Adults' Interactions with Voice Assistants: A Month-Long, In-Home Study (2026) · cites this paper as [19]
Cited by
Sorted by citations
What Makes a Good Conversation?: Challenges in Designing Truly Conversational Agents
Clark, L., Pantidi, N., Cooney, O., Doyle, P., Garaialde, D., Edwards, J., Spillane, B., Gilmartin, E., Murad, C., Munteanu, C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300705 · Cited by 461 · see citing works
Self-citationMapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
Doyle, P. R., Edwards, J., Dumbleton, O., Clark, L., & Cowan, B. R. (2019) · Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services · doi:10.1145/3338286.3340116 · Cited by 100 · see citing works
Self-citationMahmood, A., Wang, J., & Huang, C.-M. (2026) · ACM Transactions on Accessible Computing · doi:10.1145/3796236 · Cited by 6 · see citing works
“…[16, 20], which can be attributed to two factors: 1) speech interfaces are increasingly designed and marketed to emulate the nuances of human speech [23, 26] and 2) speech alone as interaction modality may inherently provide a false affordance for possible interactions—essentially tricking people…”
- Authors
- Gilmartin, E., Collery, M., Su, K., Huang, Y., Elias, C., Cowan, B. R., & Campbell, N.
- Affiliations
- Trinity College Dublin (IE)
- Institut polytechnique de Grenoble (FR)
- University College Dublin (IE)
- Venue
- Proceedings of the 1st ACM SIGCHI International Workshop on Investigating Social Interactions with Artificial Agents
- Publisher
- ACM
- Published
- 2017-11-13
- Pages
- 31-32
- OpenAlex
- W2767887487
- Cited by
- 17
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 4 · 2021: 4 · 2020: 3 · 2019: 3
- FWCI
- 0.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGCHI International Workshop on Investigating Social Interactions with Artificial Agents (ISIAA) (publishedVersion)
- Funders
- Science Foundation Ireland
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Arts and Humanities › Language and Linguistics › Language, Discourse, Communication Strategies
- Keywords
- social talk, conversation analysis, human-machine interaction, human-robot interaction, human-human dialogue, dialogue corpus, social companions, healthcare robots, guessing games
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
