GEG: Master Ranked List Of 13.9 Million Unique Entities From 103 Million English News Articles 2016-2019

The inaugural GDELT Global Entity Graph (GEG) contained more than 11.4 billion entity annotations from over 103 million English language articles 2016-2019, annotated through Google's Cloud Natural Language API. What are the most common entities found in global English language online news coverage of the past three years?

Out of the 11,416,958,078 total entity annotations, a total of 1,584,550,381 entities (13.9%) have a non-null MID. Of the entities with a MID code, there were 27,833,754 distinct entity names resolving to 13,892,261 distinct MID entity IDs (name variants of an entity all resolve to the same MID).

The entities with MID codes fall into eight "types" as seen below:

Type Count
PERSON 6332329
LOCATION 2849856
OTHER 1326627
WORK_OF_ART 795123
EVENT 253562

For each entity, its most common name, MID, most common type, most common Wikipedia URL, average salience and total number of appearances was computed. Since the Cloud Natural Language API recognizes many different alternative and variant names for entities, the 11.4 billion entities were collapsed by MID code (entities without a MID code were excluded). For each MID code, a list of every textual name associated with that MID was compiled and the most common one selected, with the same process applied to its type (since a name in different contexts can take on different type roles) and Wikipedia URL. The average salience of all mentions of the entity across the 103 million articles was also recorded, allowing entities to be examined through how "important" they are on average to the documents they appear in, while the total count reflects how often the entity appeared.

The top 10 entities appear below:

Entity MID Type WikipediaURL AvgSalience Count
U.S. /m/09c7w0 LOCATION 0.014269125 31447173
Donald Trump /m/0cqt90 PERSON 0.102800737 12805048
UK /m/07ssc LOCATION 0.010535596 8679941
The Associated Press /m/0cv_2 ORGANIZATION 0.012310849 7978374
Europe /m/02j9z LOCATION 0.005559554 7367460
New York /m/02_286 LOCATION 0.006931887 6991149
Republican /m/07wbk ORGANIZATION 0.010974562 6672985
Twitter /m/0289n8t OTHER 0.004768608 6662767
China /m/0d05w3 LOCATION 0.024354636 6460596
Democratic /m/0d075m PERSON 0.011383625 6126569

The complete list of 13,892,261 distinct MIDs is available as a UTF8 CSV file:



Compiling the dataset above took just a single SQL query and 153 seconds to process 382GB of annotations into the final dataset.

SELECT APPROX_TOP_COUNT(, 1)[OFFSET(0)].value entity,
entities.mid mid,
APPROX_TOP_COUNT(entities.type, 1)[OFFSET(0)].value type,
APPROX_TOP_COUNT(entities.wikipediaUrl, 1)[OFFSET(0)].value wikipediaurl,
avg(avgSalience) avgsalience,
count(1) count 
 FROM `gdelt-bq.gdeltv2.geg_gcnlapi`, unnest(entities) entities
where entities.mid is not null
group by entities.mid order by Count desc