Temporal relationship of suicide-related internet searches and suicide rates in Korea: A prewhitened cross-correlation analysis
Year: 2026
Type: article
Abstract: OBJECTIVE: Suicide-related internet search patterns may reflect population-level behavioral responses to suicide risk. This study investigated the temporal associations between suicide-related internet search volumes and weekly suicide rates in Korea using rigorous time-series methodologies. METHODS: Weekly suicide rates and search volumes for 25 suicide-related terms were obtained from national mortality records and Naver DataLab for two distinct periods: 2016-2019 and 2020-2023. Prewhitening was applied to mitigate spurious correlations, followed by cross-correlation analyses to assess temporal relationships at lags ranging from 0 to 8 weeks. RESULTS: Searches for "psychiatry" and "workplace stress" demonstrated consistent contemporaneous correlations across both study periods. During the 2020-2023 period, significant contemporaneous associations were also observed for prevention-related terms (e.g., "suicide crisis counseling," "1577-0199," "psychological counseling"), "fatigue," "suicide urges," and "suicide death benefit." In contrast, "depression" exhibited a significant association only during the 2016-2019 period. Across all findings, the observed effect sizes were modest. CONCLUSIONS: Specific internet search terms, particularly those related to suicide prevention resources and occupational stress, exhibit temporal associations with population-level suicide rates. These findings suggest that monitoring online help-seeking behaviors and work-related stressors could serve as useful indicators for public health planning, although further research is required to elucidate underlying mechanisms and determine practical applications.
Source: PLoS ONE
Institution: Chonnam National University
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,382
Award: G8342016800
OpenAlex ID: W7128424216