The graph represents a network of 19,136 Twitter users whose recent tweets contained "OECD", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Thursday, 13 February 2020 at 04:04 UTC.
The tweets in the network were tweeted over the 4-day, 18-hour, 24-minute period from Saturday, 08 February 2020 at 09:00 UTC to Thursday, 13 February 2020 at 03:25 UTC.
Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".
The graph is directed.
The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Author Description
Vertices : 19136
Unique Edges : 24895
Edges With Duplicates : 12379
Total Edges : 37274
Number of Edge Types : 5
MentionsInRetweet : 10401
Replies to : 2463
Retweet : 11583
Mentions : 11106
Tweet : 1721
Self-Loops : 1964
Reciprocated Vertex Pair Ratio : 0.00987181939927301
Reciprocated Edge Ratio : 0.0195506384268556
Connected Components : 1498
Single-Vertex Connected Components : 653
Maximum Vertices in a Connected Component : 14213
Maximum Edges in a Connected Component : 31584
Maximum Geodesic Distance (Diameter) : 22
Average Geodesic Distance : 5.537143
Graph Density : 7.20790624265363E-05
Modularity : 0.651315
NodeXL Version : 1.0.1.423
Data Import : The graph represents a network of 19,136 Twitter users whose recent tweets contained "OECD", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Thursday, 13 February 2020 at 04:04 UTC.
The tweets in the network were tweeted over the 4-day, 18-hour, 24-minute period from Saturday, 08 February 2020 at 09:00 UTC to Thursday, 13 February 2020 at 03:25 UTC.
Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch
Graph Term : OECD
Groups : The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
Edge Color : Edge Weight
Edge Width : Edge Weight
Edge Alpha : Edge Weight
Vertex Radius : Betweenness Centrality
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[4110] 65세,이상 [4109] 출처,oecd [4108] 이상,빈곤층 [4108] 빈곤층,비율 [4108] 비율,대한민국 [4108] 대한민국,49 [4108] 49,오스트레일리아 [4108] 오스트레일리아,35 [4108] 35,미국 [4108] 미국,21 Top Word Pairs in Tweet in G1:
[4083] 65세,이상 [4082] 이상,빈곤층 [4082] 빈곤층,비율 [4082] 비율,대한민국 [4082] 대한민국,49 [4082] 49,오스트레일리아 [4082] 오스트레일리아,35 [4082] 35,미국 [4082] 미국,21 [4082] 21,일본 Top Word Pairs in Tweet in G2:
[1195] share,people [1195] people,aged [1195] aged,over [1195] over,65 [1195] 65,living [1195] living,poverty [1195] poverty,australia [1195] australia,35 [1195] 35,21 [1195] 21,japan Top Word Pairs in Tweet in G3:
[384] oecd,oecd [384] gov,nassau [382] fnm,gov [379] bahamas,oecd [368] #new,fnm [362] nassau,bahamas [360] bahamas,bahamas [232] chips,fall [210] data,driven [161] driven,approaches Top Word Pairs in Tweet in G4:
[874] oecd,nations [869] average,american [869] american,pays [869] pays,10 [869] 10,586 [869] 586,year [869] year,health [869] health,care [869] care,costs [869] costs,people Top Word Pairs in Tweet in G5:
[195] oecd,oecd_social [190] guardian,theintercept [188] gilmarmendes,oecd [188] nytimes,nytimeses [188] nytimeses,lemondefr [188] lemondefr,japantimes [188] japantimes,elpaisinenglish [188] elpaisinenglish,pravdaru [188] pravdaru,xhnews [188] xhnews,alemanha_pt Top Word Pairs in Tweet in G6:
[68] 24시,상담톡 [68] 상담톡,evedoc [68] evedoc,홈페이지 [68] 홈페이지,미프진 [68] 미프진,은 [68] 은,현재 [68] 현재,119개국에서 [68] 119개국에서,합법으로 [68] 합법으로,인정되었고 [68] 인정되었고,oecd회원국의 Top Word Pairs in Tweet in G7:
[924] climate,change [462] country,front [462] front,line [462] line,climate [462] change,summer [462] summer,devastating [462] devastating,drought [462] drought,bushfires [462] bushfires,storms [462] storms,proven Top Word Pairs in Tweet in G8:
[289] working,international [289] international,institutions [289] institutions,including [289] including,wef [289] wef,oecd [289] oecd,united [289] united,nations [289] nations,identify [289] identify,best [289] best,practices Top Word Pairs in Tweet in G9:
[160] gt,gt [144] taxes,wealth [92] taxes,taxes [65] work,life [65] life,balance [54] oecd,countries [47] capital,taxes [47] based,taxes [46] except,saying [46] saying,fact Top Word Pairs in Tweet in G10:
[232] oecd,最も所得税が高い国は北欧 [232] 最も所得税が高い国は北欧,実は日本は重税国家でスウェーデン並みの55 [226] 保守速報,oecd [31] 韓国の市中から資金が枯渇,韓国経済が枯死しつつあると判明 [31] 韓国経済が枯死しつつあると判明,不名誉すぎる世界一位の称号を獲得 [31] 不名誉すぎる世界一位の称号を獲得,金脈硬化 [31] 金脈硬化,人に例えれば体 [31] 人に例えれば体,経済 [31] 経済,に血 [31] に血,通貨 Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
Top Replied-To in G3:
Top Replied-To in G4:
Top Replied-To in G5:
Top Replied-To in G6:
Top Replied-To in G7:
Top Replied-To in G8:
Top Replied-To in G9:
Top Mentioned in Entire Graph:
Top Mentioned in G2:
Top Mentioned in G3:
Top Mentioned in G4:
Top Mentioned in G5:
Top Mentioned in G6:
Top Mentioned in G7:
Top Mentioned in G8:
Top Mentioned in G9:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
Top Tweeters in G2:
Top Tweeters in G3:
Top Tweeters in G4:
Top Tweeters in G5:
Top Tweeters in G6:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: