The graph represents a network of 16,271 Twitter users whose recent tweets contained "#Epstein", 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 Wednesday, 30 October 2019 at 17:14 UTC.
The tweets in the network were tweeted over the 4-day, 1-hour, 11-minute period from Saturday, 26 October 2019 at 15:14 UTC to Wednesday, 30 October 2019 at 16:26 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 : 16271
Unique Edges : 21422
Edges With Duplicates : 5834
Total Edges : 27256
Self-Loops : 2010
Reciprocated Vertex Pair Ratio : 0.00377073906485671
Reciprocated Edge Ratio : 0.00751314800901578
Connected Components : 1064
Single-Vertex Connected Components : 794
Maximum Vertices in a Connected Component : 14658
Maximum Edges in a Connected Component : 25538
Maximum Geodesic Distance (Diameter) : 17
Average Geodesic Distance : 4.008993
Graph Density : 8.04444784078762E-05
Modularity : 0.647828
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 16,271 Twitter users whose recent tweets contained "#Epstein", 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 Wednesday, 30 October 2019 at 17:14 UTC.
The tweets in the network were tweeted over the 4-day, 1-hour, 11-minute period from Saturday, 26 October 2019 at 15:14 UTC to Wednesday, 30 October 2019 at 16:26 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 : #Epstein
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 : Followers
Vertex Alpha : Followers
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[7366] medical,examiner [7298] jeffrey,epstein [6889] #epstein,dead [6881] want,#epstein [6744] famous,forensic [6740] epstein,strangulated [6738] examiner,jeffrey [6737] forensic,medical [6558] #breaking,famous [6558] strangulated,want Top Word Pairs in Tweet in G1:
[6232] want,#epstein [6232] #epstein,dead [6117] medical,examiner [6116] #breaking,famous [6116] famous,forensic [6116] forensic,medical [6116] examiner,jeffrey [6116] jeffrey,epstein [6116] epstein,strangulated [6116] strangulated,want Top Word Pairs in Tweet in G2:
[792] #epstein,autopsy [792] murder,suicide [788] more,murder [784] autopsy,looks [784] looks,more [784] suicide,well [784] well,never [618] medical,examiner [469] #epstein,death [465] more,consistent Top Word Pairs in Tweet in G3:
[206] jeffrey,epstein [198] medical,examiner [178] want,#epstein [176] famous,forensic [174] #epstein,dead [172] forensic,medical [172] examiner,jeffrey [172] epstein,strangulated [118] human,trafficking [116] big,deal Top Word Pairs in Tweet in G4:
[31] jeffrey,epstein [31] #epstein,#epsteindidntkillhimself [29] #epstein,murdered [29] michael,baden [28] kill,himself [25] more,consistent [25] homicidal,strangulation [25] strangulation,suicide [25] dr,michael [22] autopsy,more Top Word Pairs in Tweet in G5:
[401] #theresistance,#cnn [401] #cnn,#msnbc [335] address,book [333] listed,#epstein's [310] people,listed [308] names,1 [308] 1,000 [308] #msnbc,#yahoo [277] release,names [269] #epstein's,address Top Word Pairs in Tweet in G6:
[189] jeffrey,epstein [161] dr,baden [157] death,homicide [155] famed,pathologist [155] pathologist,dr [155] baden,evidence [155] evidence,shows [155] shows,jeffrey [155] homicide,video [117] jeffrey,epstein's Top Word Pairs in Tweet in G7:
[278] #freeassange,#epstein [268] #epstein,#thüringenwahl [211] nuoviso,spiegelanti [200] teamkenfm,nachdenkseiten [194] nachdenkseiten,nuoviso [159] spiegelanti,telepolis_news [150] telepolis_news,rtdeutsch [130] rtdeutsch,de_sputnik [120] #kenfm,teamkenfm [108] #thüringenwahl,bild Top Word Pairs in Tweet in G8:
[131] epstein's,island [131] make,sure [129] strange,art [129] art,found [129] found,jeffrey [129] jeffrey,epstein's [129] island,video [129] video,out [129] out,later [129] later,tonight Top Word Pairs in Tweet in G9:
[97] jeffrey,epstein's [91] epstein's,autopsy [91] autopsy,more [91] more,consistent [91] consistent,homicidal [91] homicidal,strangulation [91] strangulation,suicide [91] suicide,dr [91] dr,michael [91] michael,baden Top Word Pairs in Tweet in G10:
[123] #epstein,murdered [115] donald,trump's [115] trump's,day [115] day,far [115] far,meltdown [115] meltdown,#rupublicans [115] #rupublicans,even [115] even,spell [115] spell,party [115] party,autopsy Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
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Top Replied-To in G5:
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Top Replied-To in G8:
Top Replied-To in G9:
Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
Top Mentioned in G3:
Top Mentioned in G5:
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Top Mentioned in G7:
Top Mentioned in G8:
Top Mentioned in G9:
Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G4:
Top Tweeters in G5:
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Top Tweeters in G7:
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Top Tweeters in G9:
Top Tweeters in G10: