What Googling Trends Tell Us about Public Interest in Earthquakes
Bibliographic record
Abstract
Research Article| January 24, 2018 What Googling Trends Tell Us about Public Interest in Earthquakes Yen Joe Tan; Yen Joe Tan aLamont‐Doherty Earth Observatory, Columbia University, 61 Route 9 West, P.O. Box 1000, Palisades, New York 10964 U.S.A., yjt@ldeo.columbia.edu Search for other works by this author on: GSW Google Scholar Rijan Maharjan Rijan Maharjan bDepartment of Mechanical Engineering and Materials Science, Yale University, 9 Hillhouse Avenue, M11, New Haven, Connecticut 06520 U.S.A., rijan.maharjan@yale.edu Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2018) 89 (2A): 653–657. https://doi.org/10.1785/0220170116 Article history first online: 24 Jan 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yen Joe Tan, Rijan Maharjan; What Googling Trends Tell Us about Public Interest in Earthquakes. Seismological Research Letters 2018;; 89 (2A): 653–657. doi: https://doi.org/10.1785/0220170116 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT Previous studies have shown that immediately after large earthquakes, there is a period of increased public interest. This represents a window of opportunity for science communication and disaster‐relief fundraising efforts to reach more people. However, how public interest varies for different earthquakes has not been quantified systematically on a global scale. We analyze how global search interest for the term “earthquake” on Google varies following destructive earthquakes from 2004 to 2016. We find that there is a spike in search interest after destructive earthquakes followed by an exponential temporal decay. The duration and time constant of increased search interest correlate with death toll and damages but did not correlate with earthquake magnitude, estimated population exposed to very strong shaking, and number of U.S. Geological Survey (USGS) “Did You Feel It?” (DYFI) responses. Furthermore, we obtain similar time constants of increased search interest when analyzing just the U.S. search interest following destructive earthquakes outside of U.S.A., Canada, and Mexico. This suggests that a significant portion of the increased search interest comes from people who did not feel the shaking. Our observations are consistent with more destructive earthquakes receiving more media coverage which leads to a longer duration of elevated public interest in earthquakes. Of the 73 earthquakes that resulted in an increase in global search interest that fit our selection criteria, only 11 (15%) resulted in an elevated search interest of more than a week. Therefore, to take advantage of these short durations of increased public interest, science communication and disaster‐relief fundraising efforts have to act promptly following devastating earthquakes. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".