The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
News cues: Information scent and cognitive heuristics
Abstract
Google News and other newsbots have automated the process of news selection, providing Internet users with a virtually limitless array of news and public information dynamically culled from thousands of news organizations all over the world. In order to help users cope with the resultant overload of information, news leads are typically accompanied by three cues: (a) the name of the primary source from which the headline and lead were borrowed, (b) the time elapsed since the story broke, and (c) the number of related articles written about this story by other news organizations tracked by the newsbot. This article investigates the psychological significance of these cues by positing that the information scent transmitted by each cue triggers a distinct heuristic (mental shortcut) that tends to influence online users' perceptions of a given news item, with implications for their assessment of the item's relevance to their information needs and interests. A large 2 × 3 × 6 within‐subjects online experiment (N = 523) systematically varied two levels of the source credibility cue, three levels of the upload recency cue and six levels of the number‐of‐related‐articles cue in an effort to investigate their effects upon perceived message credibility, newsworthiness, and likelihood of clicking on the news lead. Results showed evidence for source primacy effect, and some indication of a cue‐cumulation effect when source credibility is low. Findings are discussed in the context of machine and bandwagon heuristics.
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
Mixing Humans and Nonhumans Together: The Sociology of a Door-Closer
Johnson, J. (1988) · Social Problems · doi:10.2307/800624 · Cited by 892
How users assess Web pages for information seeking
Tombros, A., Ruthven, I., & Jose, J. M. (2005) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20106 · Cited by 202
Cited by
Messing, S., & Westwood, S. J. (2014) · Communication Research · doi:10.1177/0093650212466406 · Cited by 902
Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information
Sundar, S. S., & Kim, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300768 · Cited by 347
Self-citationLuo, M., Hancock, J. T., & Markowitz, D. M. (2022) · Communication Research · doi:10.1177/0093650220921321 · Cited by 245
Web credibility assessment: Conceptualization, operationalization, variability, and models
Choi, W., & Stvilia, B. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23543 · Cited by 168
Automating the News: How Personalized News Recommender System Design Choices Impact News Reception
Beam, M. A. (2014) · Communication Research · doi:10.1177/0093650213497979 · Cited by 159
Edgerly, S., Mourão, R. R., Thorson, E., & Tham, S. M. (2020) · Journalism & Mass Communication Quarterly · doi:10.1177/1077699019864680 · Cited by 145
Shin, D., Koerber, A., & Lim, J. S. (2025) · New Media & Society · doi:10.1177/14614448241234040 · Cited by 137
Source Cues in Online News: Is the Proximate Source More Powerful than Distal Sources?
Kang, H., Bae, K., Zhang, S., & Sundar, S. S. (2011) · Journalism & Mass Communication Quarterly · doi:10.1177/107769901108800403 · Cited by 122
Self-citationHow Can Psychological Science Help Counter the Spread of Fake News?
van der Linden, S., Roozenbeek, J., Maertens, R., Basol, M., Kácha, O., Rathje, S., & Traberg, C. S. (2021) · The Spanish Journal of Psychology · doi:10.1017/sjp.2021.23 · Cited by 71
“…when readers attend to source cues, source credibility affects evaluations of online news stories (Go et al., 2014; Greer, 2003; Sterrett et al., 2019; Sundar et al., 2007) and in some cases, sources impact the believability of misinformation (Amazeen & Krishna, 2020; Walter & Tukachinsky, 2020).”
BackgroundMental States: A Key Point in Scam Compliance and Warning Compliance in Real Life
Wen, X., Xu, L., Wang, J., Gao, Y., Shi, J., Zhao, K., Tao, F., & Qian, X. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19148294 · Cited by 19
Relevance versus big numbers: Students’ criteria for selecting scholarly references online.
Rouet, J.-F., Skov, O., De Pereyra, G., Ros, C., Le Bigot, L., & Vibert, N. (2018) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000194 · Cited by 6
Metadata
- Authors
- Sundar, S. S., Knobloch‐Westerwick, S., & Hastall, M. R.
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2006-12-20
- Volume
- 58
- Issue
- 3
- Pages
- 366-378
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W2060410240
- Cited by
- 302
- References
- 3
- FWCI
- 9.97
- 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.
