Measuring the credibility of generative AI-produced information: An exploratory factor analysis.
Anita Crescenzi|Bogeum Choi|Pao-Pei Huang|Siddhida Pandya|Emma Gautier|Riley Little
| Anthology ID: | DBLP:conf/chiir/CrescenziCHPGL26 |
|---|---|
| Volume: | Proceedings of the 2026 Conference on Human Information Interaction and Retrieval, CHIIR 2026, Seattle, WA, USA, March 22-26, 2026 |
| Year: | 2026 |
| Venue: | Conference on Human Information Interaction and Retrieval (CHIIR) |
| Publisher: | ACM |
| Pages: | 503-507 |
| URL: | https://doi.org/10.1145/3786304.3787882 |
| DOI: | https://doi.org/10.1145/3786304.3787882 |
| DBLP: | conf/chiir/CrescenziCHPGL26 |
| BibTeX: |
@inproceedings{crescenzi-2026-measuring,
author = {Anita Crescenzi and
Bogeum Choi and
Pao-Pei Huang and
Siddhida Pandya and
Emma Gautier and
Riley Little},
editor = {Chirag Shah and
Ryen W. White and
Adam Fourney and
Carla Teixeira Lopes and
Johanne R. Trippas},
title = {{Measuring the credibility of generative AI-produced information: An exploratory factor analysis}},
booktitle = {{Proceedings of the 2026 Conference on Human Information Interaction and Retrieval, CHIIR 2026, Seattle, WA, USA, March 22-26, 2026}},
pages = {503--507},
publisher = {ACM},
year = {2026},
url = {https://doi.org/10.1145/3786304.3787882},
doi = {https://doi.org/10.1145/3786304.3787882}
}