GDELT global-news theme attention (daily)

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Daily enrichment signals scoring which themes dominate each country's news. Each day the 96 keyless GDELT 2.1 GKG fifteen-minute bulk files for the last complete UTC day are downloaded; articles are deduplicated on URL, attributed to one country (first country-level V2ENHANCEDLOCATIONS entry, FIPS 10-4 code resolved to ISO alpha-3), and their V1THEMES topic codes are aggregated per (country, theme) with GDELT's own per-article V1.5TONE scores. Per country-theme cell: n_articles, n_sources, mean_tone, pos_share / neg_share (fraction of articles with tone >= 1.0 / <= -1.0), theme_share_pct (cell articles over the country's themed articles; shares sum above 100 because articles carry several themes), theme_rank (per country, n_articles desc, theme asc), attention_score = 100 * (50% global min-max of n_articles + 50% global min-max of theme_share_pct; 100 = the day's most-attended cell worldwide), and theme_concentration (per-country Herfindahl of theme shares, 0-10000; high = a few themes dominate). Who joins this: a media analyst joins daily country theme attention to the gdelt event/conflict tables on country_code + as_of; a comms team tracks a theme's salience per market. Primary key: (as_of, country_code, theme); join keys: country_code (ISO alpha-3), as_of, theme. Nullability: none — every column is populated for every kept cell.

Source: GDELT global-news theme attention (agent-curated)
Rows: 33,869
Columns: 14
Source cadence: Daily
Last refreshed: Sep 28, 2026
Theme: news

Column	Type	Description
as_of	string	Snapshot date (UTC, YYYY-MM-DD) — the complete UTC day whose 96 GKG slots were collected. Part of the primary key. (unit: date)
country_code	string	Article-attribution country, ISO alpha-3, from the first country-level V2ENHANCEDLOCATIONS geocode (FIPS 10-4 resolved via the audited crosswalk). Part of the primary key; the join key for country tables. (unit: ISO alpha-3)
country_name	string	Canonical English short name for country_code. (unit: text)
theme	string	GDELT V1THEMES topic code (e.g. TAX_FNCACT, ECON_INFLATION, LEADER). Part of the primary key. (unit: code)
n_articles	integer	Number of distinct GDELT articles (URL- deduplicated within the day) attributed to the country and carrying the theme. (unit: count)
n_sources	integer	Number of distinct publisher domains behind the cell's articles. (unit: count)
mean_tone	float	Mean of GDELT's per-article V1.5TONE tone values (roughly [-10, 10]; positive is favorable). (unit: score)
pos_share	float	Fraction of the cell's articles with tone >= 1.0. (unit: share)
neg_share	float	Fraction of the cell's articles with tone <= -1.0. (unit: share)
theme_share_pct	float	100 * cell n_articles / the country's total themed articles. Shares sum above 100 within a country because articles carry several themes. (unit: percent)
theme_rank	integer	Per-country theme rank: 1 = most articles; ties broken by theme code ascending. (unit: rank)
attention_score	float	0-100 composite: 50% global min-max of n_articles + 50% global min-max of theme_share_pct. 100 = the day's most-attended theme-country cell worldwide. (unit: score)
theme_concentration	float	Per-country Herfindahl index (0-10000) of the theme shares renormalized to sum to 100 (raw shares sum above 100 because articles carry several themes); high values mean a few themes dominate that country's news day. (unit: index)
row_hash	string	Deterministic 16-hex-char hash of (as_of, country_code, theme, n_articles, mean_tone) for change detection. (unit: hash)

First 10 sample rows — a preview, not the complete dataset.

as_of	country_code	country_name	theme	n_articles	n_sources	mean_tone	pos_share	neg_share	theme_share_pct	theme_rank	attention_score	theme_concentration	row_hash
2026-09-27	AFG	Afghanistan	TAX_FNCACT	96	70	-4.251	0.177	0.76	94.118	1	48.32	85.46	6f78cf9a58fa223d
2026-09-27	AFG	Afghanistan	TAX_WORLDLANGUAGES	84	61	-4.822	0.155	0.786	82.353	2	42.27	85.46	8a26d0cc871750af
2026-09-27	AFG	Afghanistan	CRISISLEX_CRISISLEXREC	74	59	-5.662	0.095	0.851	72.549	3	37.23	85.46	a0e15a7bede5448c
2026-09-27	AFG	Afghanistan	EPU_POLICY	74	60	-5.051	0.081	0.865	72.549	4	37.23	85.46	f85d387ff1cb6908
2026-09-27	AFG	Afghanistan	TAX_WORLDLANGUAGES_AFGHAN	74	54	-5.096	0.149	0.811	72.549	5	37.23	85.46	8d65f84b20292e84
2026-09-27	AFG	Afghanistan	ARMEDCONFLICT	67	55	-6.88	0	0.985	65.686	6	33.7	85.46	2b849b2afd849a75
2026-09-27	AFG	Afghanistan	GENERAL_GOVERNMENT	62	52	-5.27	0.065	0.887	60.784	7	31.18	85.46	db11dc4f9d75130f
2026-09-27	AFG	Afghanistan	TAX_TERROR_GROUP	59	47	-6.293	0.034	0.915	57.843	8	29.67	85.46	dd1537c80b2d3adf
2026-09-27	AFG	Afghanistan	KILL	58	52	-6.623	0.034	0.948	56.863	9	29.16	85.46	57189024b63164a5
2026-09-27	AFG	Afghanistan	EPU_POLICY_GOVERNMENT	57	47	-5.328	0.07	0.877	55.882	10	28.66	85.46	dcf8b7b8dbfb5889

Profiled Sep 28, 2026 from snapshot 20260928T054847Z-78c4b1361a6c

Measured

Completeness: 100%
Rows: 33,869
Columns: 14
Columns with gaps: 0

Column	Missing	Distinct	Range	Distribution
as_ofvarchar	0%	1	—	2026-09-2733,869
country_codevarchar	0%	186	—	USA2,045 GBR1,517 IND1,324
country_namevarchar	0%	166	—	United States2,045 United Kingdom1,517 India1,324
themevarchar	0%	2,778	—	TAX_FNCACT155 TAX_ETHNICITY146 UNGP_FORESTS_RIVERS_OCEANS146
n_articlesbigint	0%	710	3 → 3,688median 7	339 outside 1st–99th percentile
n_sourcesbigint	0%	619	1 → 1,432median 6	339 outside 1st–99th percentile
mean_tonedouble	0%	8,647	-14.91 → 11.32median -1.36	668 outside 1st–99th percentile
pos_sharedouble	0%	706	0 → 1median 0.154
neg_sharedouble	0%	1,092	0 → 1median 0.565
theme_share_pctdouble	0%	4,176	0.072 → 100median 4.27	597 outside 1st–99th percentile
theme_rankbigint	0%	2,351	1 → 2,045median 187	651 outside 1st–99th percentile
attention_scoredouble	0%	2,342	0 → 94.32median 2.33	597 outside 1st–99th percentile
theme_concentrationdouble	0%	176	43.77 → 5,313median 67.77	317 outside 1st–99th percentile
row_hashvarchar	0%	27,966	—	00004a573740a12c1 0007061dc9bb8c961 0009c395533323f31

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/gdelt_theme_signals/gdelt_theme_attention_daily" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/gdelt_theme_signals/gdelt_theme_attention_daily").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/gdelt_theme_signals/gdelt_theme_attention_daily

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20260928T054847Z-78c4b1361a6c and its content hash, so readers get exactly the data you used.

APA

GDELT global-news theme attention (agent-curated). (2026). GDELT global-news theme attention (daily) [Data set, snapshot 20260928T054847Z-78c4b1361a6c, sha256 78c4b1361a6c]. Datazimuts. Retrieved 2026-09-28, from https://datazimuts.com/en/datasets/gdelt_theme_signals/gdelt_theme_attention_daily?snapshot=20260928T054847Z-78c4b1361a6c

BibTeX

@misc{dz_gdelt_theme_signals_gdelt_theme_attentio_78c4b136,
  title        = {{GDELT global-news theme attention (daily)}},
  author       = {{GDELT global-news theme attention (agent-curated)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/gdelt_theme_signals/gdelt_theme_attention_daily?snapshot=20260928T054847Z-78c4b1361a6c}},
  note         = {Snapshot 20260928T054847Z-78c4b1361a6c, sha256 78c4b1361a6c901ff0777fcfe073ace9171f8b3c12db47ade53c907331dabbef; accessed 2026-09-28}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=gdelt_theme_signals%2Fgdelt_theme_attention_daily&lang=en&theme=auto&snapshot=20260928T054847Z-78c4b1361a6c&x=as_of&y=n_articles&agg=avg" title="GDELT global-news theme attention (daily)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

GDELT global-news theme attention (daily)

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