How does turning to AI for companionship predict loneliness and vice versa?
Bibliographic record
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 2000 adults from four Western countries, we examined the bi-directional relationships between social chatbot use and loneliness. We found consistent 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 consistent 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".