Cited by 33

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

How Does Turning to AI for Companionship Predict Loneliness and Vice Versa?

Folk, Dunigan, Dunn, Elizabeth·2026·Psychological Science

Abstract

Advances in AI have enabled chatbots to provide warm, personalized support. Yet little is known about the long-term consequences of AI companionship. Across a 12-month longitudinal study with more than 2,000 adults from four Western countries, we examined the bidirectional relationships between social chatbot use and loneliness.We found evidence that increased social chatbot use predicted increased loneliness, using a single-item measure of emotional isolation. When we used a broader and more stable measure of social connection, we found evidence that feeling less socially connected predicted subsequent increases in social chatbot use; however, chatbot use did not significantly predict decreases in social connection. Taken together, these findings provide initial evidence that being lonely may spur people to seek companionship through chatbots but that such use may, over time, exacerbate feelings of loneliness. We urge caution, however, in drawing strong conclusions given the exploratory nature of our analyses.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/09567976261427747

Cites

14 of 14 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

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

Flagged first, then by citations

Research works printed but not linked: 16

  1. Altman I., Taylor D. A. (1973). Social penetration: The development of interpersonal relationships. Holt, Rinehart & Winston.
  2. 10.1177/02654075211019129 · DOI
  3. 10.1037/0033-2909.116.3.457 · DOI · Paper page
  4. Cunningham K. B. Wells M. Kroll T. (2025). Back to basics in the field of loneliness: Progressing conceptualisation and definition of the term–an umbrella concept analysis. Health Psychology Review 19(4) 839–877. https://doi.org/10.1080/17437199.2025.2528822 · DOI
  5. Flake J. K. Pek J. Hehman E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science 8(4) 370–378. https://doi.org/10.1177/1948550617693063 · DOI
  6. Folk D. Yu S. Dunn E. (2024). Can chatbots ever provide more social connection than humans? Collabra: Psychology 10(1) Article 117083. https://doi.org/10.1525/collabra.117083 · DOI · Paper page
  7. Helliwell J. F. Huang H. Norton M. Goff L. Wang S. (2023). World happiness trust and social connections in times of crisis. In Helliwell J. F. Layard R. Sachs J. D. Aknin L. B. De Neve J.-E Wang S. (Eds.) World Happiness Report 2023 (11th ed. pp. 31–76). Sustainable Development Solutions Network. https://www.worldhappiness.report/ed/2023/world-happiness-trust-and-social-connections-in-times-of-crisis
  8. Hernán M. A., Robins J. M. (2010). Causal inference. CRC Press.
  9. Inzlicht M. Cameron C. D. D’Cruz J. Bloom P. (2024). In praise of empathic AI. Trends in Cognitive Sciences 28(2) 89–91. https://doi.org/10.1016/j.tics.2023.12.003 · DOI
  10. 10.1177/0276236615595232 · DOI
  11. Mulder J. D. Luijken K. Penning de Vries B. B. Hamaker E. L. (2024). Causal effects of time-varying exposures: A comparison of structural equation modeling and marginal structural models in cross-lagged panel research. Structural Equation Modeling: A Multidisciplinary Journal 31(4) 575–591. https://doi.org/10.1080/10705511.2024.2316586 · DOI
  12. 10.1177/01461672211049049 · DOI
  13. 10.1037/pspp0000358 · DOI
  14. 10.18637/jss.v048.i02 · DOI · Paper page
  15. 10.1038/s42256-024-00841-7 · DOI · Paper page
  16. 10.1080/15205436.2022.2127369 · DOI

Cited by

Sorted by citations

Metadata

Authors
Folk, D., & Dunn, E.
Affiliations
Department of Psychology, University of British Columbia
Venue
Psychological Science
Published
2026-03-23
Volume
37
Issue
4
Pages
276-286
ISSN
  • 0956-7976
  • 1467-9280
Cited by
33
References
14
Language
en
Funders
  • social sciences and humanities research council of canada
  • MindCORE
  • walton family foundation

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