Free trade was a central economic topic this year, but who were the people and the connections between them that defined the global media discourse about free trade?

The graph below shows the top 1,500 most commonly co-occurring names in worldwide free trade news coverage in the 65 languages monitored by GDELT. (Click to enlarge).

You can download the relevant files below:

• Full Resolution Image (6K x 6K)
• Gephi File
• CSV Edge List

TECHNICAL DETAILS

Creating the graph above required just a single SQL query in BigQuery using a modified version of the original graph code from Felipe Hoffa:

SELECT Source, Target, Count RawCount, "Undirected" Type, ( Count/SUM(Count) OVER () ) Weight FROM (
SELECT a.entity Source, b.entity Target, COUNT(*) as Count
FROM (
 (SELECT DocumentIdentifier url, entity FROM `gdelt-bq.gdeltv2.gkg_partitioned`, UNNEST(SPLIT(REGEXP_REPLACE(V2Persons, r',\d+', ''),';')) AS entity WHERE V2Themes like '%ECON_FREETRADE%ECON_FREETRADE%' and DATE(_PARTITIONTIME) >= "2019-01-01" group by url,entity)
) a
JOIN (
 (SELECT DocumentIdentifier url, entity FROM `gdelt-bq.gdeltv2.gkg_partitioned`, UNNEST(SPLIT(REGEXP_REPLACE(V2Persons, r',\d+', ''),';')) AS entity WHERE V2Themes like '%ECON_FREETRADE%ECON_FREETRADE%' and DATE(_PARTITIONTIME) >= "2019-01-01" group by url,entity)
) b
ON a.url=b.url
WHERE a.entity<b.entity
GROUP BY 1,2
ORDER BY 3 DESC
LIMIT 1500
)
order by Count Desc