iCoRe: The GDELT Interface for the Advancement of Communication Research
Bibliographic record
Abstract
This article introduces the interface for communication research (iCoRe) to access, explore, and analyze the Global Database of Events, Language and Tone (GDELT; Leetaru & Schrodt, 2013). GDELT provides a vast, open source, and continuously updated repository of online news and event metadata collected from tens of thousands of news outlets around the world. Despite GDELT’s promise for advancing communication science, its massive scale and complex data structures have hindered efforts of communication scholars aiming to access and analyze GDELT. We thus developed iCoRe, an easy-to-use web interface that (a) provides fast access to the data available in GDELT, (b) shapes and processes GDELT for theory-driven applications within communication research, and (c) enables replicability through transparent query and analysis protocols. After providing an overview of how GDELT’s data pertain to addressing communication research questions, we provide a tutorial of utilizing iCoRe across three theory-driven case studies. We conclude this article with a discussion and outlook of iCoRe’s future potential for advancing communication research.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.161 | 0.084 |
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".