The graph represents a network of 6,403 Twitter users whose recent tweets contained "Pharma", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/3/2023 5:00:35 PM. The network was obtained from Twitter on Saturday, 04 February 2023 at 13:10 UTC.
The tweets in the network were tweeted over the 1487-day, 5-hour, 59-minute period from Tuesday, 08 January 2019 at 19:00 UTC to Saturday, 04 February 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
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 : 6403
Unique Edges : 3247
Edges With Duplicates : 10979
Total Edges : 14226
Number of Edge Types : 9
Retweet : 3455
MentionsInRetweet : 4171
MentionsInReplyTo : 3468
Replies to : 1619
Tweet : 827
Mentions : 354
MentionsInQuote : 149
Quote : 143
MentionsInQuoteReply : 40
Self-Loops : 1051
Reciprocated Vertex Pair Ratio : 0.0271997824017408
Reciprocated Edge Ratio : 0.0529590891036674
Connected Components : 899
Single-Vertex Connected Components : 265
Maximum Vertices in a Connected Component : 3562
Maximum Edges in a Connected Component : 8611
Maximum Geodesic Distance (Diameter) : 22
Average Geodesic Distance : 5.855333
Graph Density : 0.000184255437511402
Modularity : 0.525853
NodeXL Version : 1.0.1.510
Data Import : The graph represents a network of 6,403 Twitter users whose recent tweets contained "Pharma", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/3/2023 5:00:35 PM. The network was obtained from Twitter on Saturday, 04 February 2023 at 13:10 UTC.
The tweets in the network were tweeted over the 1487-day, 5-hour, 59-minute period from Tuesday, 08 January 2019 at 19:00 UTC to Saturday, 04 February 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch2
Graph Term : Pharma
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:
[2473] big,pharma [500] mrna,vaccines [490] data,shows [489] covid,deaths [489] deaths,canada [488] caused,mrna [488] being,caused [488] 100,covid [487] canada,being [487] vaccines,forced Top Word Pairs in Tweet in G1:
[465] mrna,vaccines [464] shows,100 [464] vaccines,forced [464] data,shows [464] deaths,canada [464] canada,being [464] caused,mrna [464] covid,deaths [464] 100,covid [464] being,caused Top Word Pairs in Tweet in G2:
[437] big,pharma [430] crush,america [430] destroy,small [430] small,businesses [430] economy,force [430] kids,destroy [430] america,economy [430] pharma,china [430] china,crush [429] vaccines,kids Top Word Pairs in Tweet in G3:
[179] big,pharma [113] big,tech [97] matthewloop,project_veritas [94] twitter,files [94] tech,pharma [94] pharma,gov [93] gov,elon [93] elon,twitter [93] together,big [93] project_veritas,colluding Top Word Pairs in Tweet in G4:
[226] big,pharma [213] accepterez,excuses [213] excuses,politiciens [213] médias,médecins [213] politiciens,médias [212] corrompus,big [212] pharma,non [212] médecins,corrompus [210] silvano_trotta,accepterez [151] 50,enfants Top Word Pairs in Tweet in G5:
[104] big,pharma [29] karen,kingston [27] kingston,big [27] bioweapon,world [27] unleashed,bioweapon [27] pharma,unleashed [26] world,time [26] press,criminal [26] criminal,charges [26] time,press Top Word Pairs in Tweet in G6:
[176] big,pharma [159] more,bullshit [159] fallout,dod [159] pharma,itself [159] protect,big [159] itself,fallout [159] government,feeding [159] bullshit,protect [159] feeding,more [159] dod,mrna Top Word Pairs in Tweet in G7:
[159] twitter,account [159] videos,exposing [159] locked,releasing [159] breaking,project [159] veritas,twitter [159] project,veritas [159] releasing,various [159] various,bombshell [159] exposing,pfizer [159] account,locked Top Word Pairs in Tweet in G8:
[84] vitamin,deficient [84] jimmy_dore,knew [84] covid,people [84] deficient,sickest [84] people,vitamin [84] never,told [84] beginning,covid [84] sickest,never [84] knew,beginning [20] big,pharma Top Word Pairs in Tweet in G9:
[92] bitchute,dr [46] brook,bitchute [46] dr,shawn [46] sherri,tenpenny [46] dr,sherri [46] shawn,brook [46] dr,judy [46] healthbyjames,dr [46] tenpenny,bitchute [46] judy,mikovits Top Word Pairs in Tweet in G10:
[58] #davos,making [58] making,#covid19 [58] unleashed,moderna [58] moderna,ceo [58] before,unleashed [58] petersweden7,created [58] ceo,admits [58] admits,#davos [58] created,before [57] versecannon,petersweden7 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 G6:
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 G4:
Top Mentioned in G5:
Top Mentioned in G6:
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:
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: