The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Robot Wrote This?: How perceived machine authorship affects news credibility
Abstract
As more news articles are written via collaboration between journalists and algorithms, questions have arisen regarding how automation influences the way that news is processed and evaluated by audiences. Informed by expectancy violation theory and the MAIN model, two experimental studies were conducted that examined the effect of purported machine authorship on perceptions of news credibility. Study One (N = 129) revealed that news attributed to a machine is perceived as less credible than news attributed to a human journalist. Study Two (N = 182) also observed negative effects of machine authorship through the indirect pathway of source anthropomorphism and negative expectancy violations, with evidence of moderation by prior recall of robotics also observed. The theoretical and practical implications of these findings are discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 1×
“…expectancy violation effects might also be elicited by the metaphors used to describe automation (e.g. “robot reporter”), as the act of anthropomorphizing technology can heighten users’ subsequent expectations of entities’ social abilities (e.g. Nass 2011; Nass and Moon 2000; Reeves and Nass 1996).”
BackgroundBartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-008-0001-3 · Cited by 3,193
Carlson, M. (2015) · Digital Journalism · doi:10.1080/21670811.2014.976412 · Cited by 542
Cited by
AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations
Hancock, J. T., Naaman, M., & Levy, K. (2020) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmz022 · Cited by 658
Jakesch, M., French, M., Ma, X., Hancock, J. T., & Naaman, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300469 · Cited by 190
Graefe, A., & Bohlken, N. (2020) · Media and Communication · doi:10.17645/mac.v8i3.3019 · Cited by 154
“9 Waddell 1 612 47 38 USA Commercial online English X Khan Conflict, 3 (attribution) × X X X 7 (2019b) panel (Amazon Paris Accord 2 (media outlet) × Mechanical Turk) 2 (topic)”
BackgroundWu, L., & Jing Wen, T. (2021) · Journal of Advertising Research · doi:10.2501/jar-2021-004 · Cited by 127
Hong, J. W., Peng, Q., & Williams, D. (2021) · New Media & Society · doi:10.1177/1461444820925798 · Cited by 102
Disinformation, social media, bots, and astroturfing: the fourth wave of digital democracy
García-Orosa, B. (2021) · El Profesional de la información · doi:10.3145/epi.2021.nov.03 · Cited by 80
Moravec, V., Hynek, N., Skare, M., Gavurova, B., & Kubak, M. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123162 · Cited by 76
Zhan, E. S., Zheng, Q., Dong, C., & Thorson, E. (2025) · International Journal of Strategic Communication · doi:10.1080/1553118x.2025.2471527 · Cited by 22
Examining Agency, Expertise, and Roles of AI Systems in AI-Mediated Communication
Mieczkowski, H., & Hancock, J. (2022) · doi:10.31219/osf.io/asnv4 · Cited by 16
Buckley, C., Norman, M., Troy, C., Yang, Y., Bao, J., Overton, H. K., & Jung, Y. (2025) · International Journal of Strategic Communication · doi:10.1080/1553118x.2025.2514276 · Cited by 12
Metadata
- Authors
- Waddell, T. F.
- Venue
- Digital Journalism
- Publisher
- Informa UK Limited
- Published
- 2017-10-20
- Volume
- 6
- Issue
- 2
- Pages
- 236-255
- ISSN
- 2167-0811 (ISSN-L)
- 2167-082X
- OpenAlex
- W2765085847
- Cited by
- 169
- References
- 3
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.