No detectable change in mental-health information-seeking during Italy's 2023 ChatGPT ban: a natural experiment with a verified first stage

Regulatory bans on AI platforms are accelerating globally, yet their behavioural consequences for affected populations remain unmeasured. Italy's temporary ChatGPT ban (31 March – 28 April 2023) provides a rare natural experiment: an exogenous, country-specific shock to AI access. I verify the first stage using Google Trends data on VPN and ChatGPT searches — Italian VPN searches peaked at nearly five times their pre-ban level on the first day of the ban, averaged twice their baseline throughout, and returned to pre-ban levels within a week of reinstatement; no equivalent change occurred in France or Spain. Against this confirmed exposure, I find no detectable change in loneliness-related information-seeking. The 2023 ban-window deviation (+3.5 index points above the preceding eight weeks) was smaller than in 2019 and 2022 (+19.3 and +19.4 points respectively) but larger than 2024 (−12.0 points); year-permutation tests return p ≥ 0.25 under both calendar and Easter alignment. An apparent "rise then fall" pattern in an earlier, single-draw version of this analysis reflected seasonal variation and single-draw sampling noise: three independent draws correlate at r ≈ 0.74 and show no consistent post-reinstatement decline. No construct shows a ban effect once seasonality is accounted for: the only secondary signal, a rise in sadness-related search that survives multiplicity correction and exceeds all placebo windows, is matched or exceeded by the same spring weeks in other years. I identify three methodological lessons for Google Trends research: the consequences of retaining sparse terms, relying on a single data pull, and using grammatically gendered phrases whose baseline asymmetry may reflect lexical rather than demographic differences.

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Publication Details

Published
2026-10-28
DOI
https://doi.org/10.31235/osf.io/4c5wf_v2
Primary Topic
Data-Driven Disease Surveillance
Type
preprint
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preprint

No detectable change in mental-health information-seeking during Italy's 2023 ChatGPT ban: a natural experiment with a verified first stage

Marta Antonetti
1 links
Data-Driven Disease Surveillance
preprint

No detectable change in mental-health information-seeking during Italy's 2023 ChatGPT ban: a natural experiment with a verified first stage

Marta Antonetti
preprint en

Abstract

Regulatory bans on AI platforms are accelerating globally, yet their behavioural consequences for affected populations remain unmeasured. Italy's temporary ChatGPT ban (31 March – 28 April 2023) provides a rare natural experiment: an exogenous, country-specific shock to AI access. I verify the first stage using Google Trends data on VPN and ChatGPT searches — Italian VPN searches peaked at nearly five times their pre-ban level on the first day of the ban, averaged twice their baseline throughout, and returned to pre-ban levels within a week of reinstatement; no equivalent change occurred in France or Spain. Against this confirmed exposure, I find no detectable change in loneliness-related information-seeking. The 2023 ban-window deviation (+3.5 index points above the preceding eight weeks) was smaller than in 2019 and 2022 (+19.3 and +19.4 points respectively) but larger than 2024 (−12.0 points); year-permutation tests return p ≥ 0.25 under both calendar and Easter alignment. An apparent "rise then fall" pattern in an earlier, single-draw version of this analysis reflected seasonal variation and single-draw sampling noise: three independent draws correlate at r ≈ 0.74 and show no consistent post-reinstatement decline. No construct shows a ban effect once seasonality is accounted for: the only secondary signal, a rise in sadness-related search that survives multiplicity correction and exceeds all placebo windows, is matched or exceeded by the same spring weeks in other years. I identify three methodological lessons for Google Trends research: the consequences of retaining sparse terms, relying on a single data pull, and using grammatically gendered phrases whose baseline asymmetry may reflect lexical rather than demographic differences.

Good health and well-being
Data-Driven Disease Surveillance
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