Supplementary material from "Appetitive information seeking behaviour reveals robust daily rhythmicity for Internet-based food-related keyword searches"
Bibliographic record
Abstract
There has been an exponential growth of information seeking behaviour (ISB) via Internet-based programs over the past decade. The availability of software that record ISB temporal patterns has provided a valuable opportunity to examine biological rhythms in human behaviour. Internet search repositories, such as Google Trends, permit the analyses of large datasets that can be used to track ISB on a domestic and international scale. We examined daily and seasonal Google Trends search patterns for keywords related to food intake, using the most relevant search terms for the USA, UK, Canada, India and Australia. Daily and seasonal ISB rhythmicity were analysed using CircWave 1.4. Daily ISB data revealed a robust and significant sine waveform for general terms (e.g. ‘pizza delivery') and country-specific search terms (e.g. ‘just eat'). The pattern revealed clear evening double-peaks, occurring every day at 19.00 h and 02.00 h. The patterns were consistent across search terms, days of the week and geographical locations, suggesting a common ISB rhythm that is not necessarily culture-dependent. Then, we conducted Cosinor 2.4 analyses to examine the daily amplitudes in ISB. The results indicated a non-significant linear increased from Monday to Sunday. Seasonal data did not show consistent significant ISB patterns. It is likely that two different human populations are responsible for the daily ‘early' and ‘late' evening ISB peaks. We propose that the major factor that contributes to the bimodal evening peak is age-dependent (e.g. adolescent, early adulthood versus midlife and mature adulthood) and a minor role for human chronotypes (e.g. late versus early). Overall, we present novel human appetitive behaviour for information seeking of food resources and propose that Internet-based search patterns reflect a biological rhythm of motivation for energy balance.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.655 | 0.206 |
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".