A Review of Smartphone Fact-Checking Apps and their (Non) Use Among Older Adults
Abstract
Falsehoods continue to spread faster than facts in today’s age of continuous, digital connections. Recent studies show older adults engage more frequently with misinformation than younger users. Given the reported uptake of mobile technologies among older adults (61% of US adults over 65 owned a smartphone in 2021), much of these engagements may occur via smartphones or tablets. What types of fact-checkers are currently available on smartphones? How do older adults fact-check information they encounter in their daily lives? In this paper, we explore these questions with a systematic app review and a semi-structured interview with older adults. Among the 8372 unique smartphone fact-checking apps identified, 45 apps were qualitatively and systematically reviewed. Five distinct user interface (UI) elements emerged: news feed, article view, fact-checking widget, learning tool, and search view. An interactive fact-checking option was found in 73% of the apps. None of our older interviewees reported using a smartphone fact-checking app but described other fact-checking behaviors, such as asking friends or acquaintances with domain expertise or searching on Google.
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?
While non-use could be a deliberate choice, another reason for limited use repeatedly observed in the HCI literature is a lack of exploration support.
“While non-use could be a deliberate choice [33,52], another reason for limited use repeatedly observed in the HCI literature is a lack of exploration support [42,69].”
From "Where is history": Toward Designing a Voice Assistant to help Older Adults locate Interface Features quickly (2023) · cites this paper as [52]
Cites
Tandoc, E. C., Lim, Z. W., & Ling, R. S. (2017) · Digital Journalism · doi:10.1080/21670811.2017.1360143 · Cited by 2,377 · see citing works
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184 · see citing works
Lewis, T. H. (1997) · JAMA · doi:10.1001/jama.1997.03540400078041 · Cited by 1,004 · see citing works
Designing for dynamic diversity
Gregor, P., Newell, A. F., & Zajicek, M. (2002) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/638249.638277 · Cited by 364 · see citing works
Bronstein, M. V., Pennycook, G., Bear, A., Rand, D. G., & Cannon, T. D. (2019) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2018.09.005 · Cited by 324 · see citing works
Roque, N. A., & Boot, W. R. (2016) · Journal of Applied Gerontology · doi:10.1177/0733464816642582 · Cited by 316 · see citing works
Hassan, N., Zhang, G., Arslan, F., Caraballo, J., Jimenez, D., Gawsane, S., Hasan, S., Joseph, M., Kulkarni, A., Nayak, A. K., et al. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137765.3137815 · Cited by 208 · see citing works
Fake news: Acceptance by demographics and culture on social media
Rampersad, G., & Althiyabi, T. (2019) · Journal of Information Technology & Politics · doi:10.1080/19331681.2019.1686676 · Cited by 183 · see citing works
Are Smartphones Ubiquitous?: An in-depth survey of smartphone adoption by seniors
Berenguer, A., Goncalves, J., Hosio, S., Ferreira, D., Anagnostopoulos, T., & Kostakos, V. (2017) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2016.2614524 · Cited by 150 · see citing works
Digital Threats to Democracy: Comparative Lessons and Possible Remedies
Miller, M. L., & Vaccari, C. (2020) · The International Journal of Press/Politics · doi:10.1177/1940161220922323 · Cited by 132 · see citing works
Amazeen, M. A., Vargo, C. J., & Hopp, T. (2018) · Communication Monographs · doi:10.1080/03637751.2018.1521984 · Cited by 118 · see citing works
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities
Weld, G., Glenski, M., & Althoff, T. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18104 · Cited by 31 · see citing works
How does fake news spread? Understanding pathways of disinformation spread through APIs
Ng, L. H. X., & Taeihagh, A. (2021) · Policy & Internet · doi:10.1002/poi3.268 · Cited by 26 · see citing works
Studying effectiveness of Web search for fact checking
Hasanain, M., & Elsayed, T. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24577 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Yu, J. E., Parde, N., & Chattopadhyay, D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581447 · Cited by 22 · see citing works
Self-citation
- Authors
- Sakhnini, N., & Chattopadhyay, D.
- Affiliations
- University of Illinois Chicago (US)
- Venue
- Adjunct Publication of the 24th International Conference on Human-Computer Interaction with Mobile Devices and Services
- Publisher
- ACM
- Published
- 2022-09-28
- Pages
- 1-8
- OpenAlex
- W4296849161
- Cited by
- 13
- References
- 14
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 2
- FWCI
- 7.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- older adults, misinformation, mobile application, user interface design, digital divide, mobile technology adoption
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
