The graph represents a network of 8,399 Twitter users whose recent tweets contained "Bundestag", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 10,000 tweets, tweeted between 7/1/2023 5:10:56 PM and 8/25/2023 5:10:56 PM. The network was obtained from Twitter on Friday, 25 August 2023 at 17:34 UTC.
The tweets in the network were tweeted over the 1741-day, 4-hour, 24-minute period from Sunday, 18 November 2018 at 15:10 UTC to Friday, 25 August 2023 at 19:34 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 : 8399
Unique Edges : 11132
Edges With Duplicates : 2702
Total Edges : 13834
Number of Edge Types : 9
Retweet : 7106
MentionsInRetweet : 1126
Mentions : 435
Replies to : 2215
MentionsInReplyTo : 1878
Tweet : 560
Quote : 235
MentionsInQuote : 236
MentionsInQuoteReply : 43
Self-Loops : 913
Reciprocated Vertex Pair Ratio : 0.00578998157733134
Reciprocated Edge Ratio : 0.0115133013519407
Connected Components : 586
Single-Vertex Connected Components : 219
Maximum Vertices in a Connected Component : 7130
Maximum Edges in a Connected Component : 12708
Maximum Geodesic Distance (Diameter) : 17
Average Geodesic Distance : 5.228856
Graph Density : 0.000162543874440875
Modularity : 0.671904
NodeXL Version : 1.0.1.520
Data Import : The graph represents a network of 8,399 Twitter users whose recent tweets contained "Bundestag", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 10,000 tweets, tweeted between 7/1/2023 5:10:56 PM and 8/25/2023 5:10:56 PM. The network was obtained from Twitter on Friday, 25 August 2023 at 17:34 UTC.
The tweets in the network were tweeted over the 1741-day, 4-hour, 24-minute period from Sunday, 18 November 2018 at 15:10 UTC to Friday, 25 August 2023 at 19:34 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 : TwitterSearch3
Graph Term : Bundestag
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 : In-Degree
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[744] #selbstbestimmungsgesetz,bundestag [735] namentliche,abstimmung [733] bundestag,namentliche [732] hoffe,abstimmung [732] abstimmung,#selbstbestimmungsgesetz [731] ainyrockstar,hoffe [731] abstimmung,abgeord [719] heizungs,verbot [719] september,habecks [719] habecks,heizungs Top Word Pairs in Tweet in G1:
[366] september,habecks [366] heizungs,verbot [366] habecks,heizungs [219] afd,einzige [195] verbot,stimmen [195] genau,politiker [195] stimmen,somit [195] merken,september [195] politiker,merken [194] nikitheblogger,genau Top Word Pairs in Tweet in G2:
[21] gchq,gchq [18] bundeskanzler,bundestag [17] gesetz,braucht [17] #bundestag,besitzobergrenze [17] #cannabis,gesetz [17] braucht,weitreichende [17] änderungen,#bundestag [17] weitreichende,änderungen [16] kristine_lutke,#cannabis [15] deutschen,bundestag Top Word Pairs in Tweet in G3:
[90] massiv,bedroht [90] schutzräume,frauen [90] frauen,massiv [89] #ampelkoalition,bedeutet [89] bedeutet,schutzräume [89] #selbstbestimmungsgesetz,#ampelkoalition [88] tsg,frau [88] christophploss,#selbstbestimmungsgesetz [88] absolut,personenstandsänderung [88] verfahren,tsg Top Word Pairs in Tweet in G4:
[99] september,habecks [99] heizungs,verbot [99] habecks,heizungs [98] verabschiedet,dürfte [98] dürfte,#deutschland [98] #gesetz,deutsche [98] #bundestag,verabschiedet [98] deutsche,#bundestag [98] durchgeknallteste,#gesetz [97] #deutschland,#wirt Top Word Pairs in Tweet in G5:
[580] luftbrücke,kabul [538] 44,000 [538] afghanen,obwohl [538] januar,luftbrücke [538] baerbock,fliegt [538] fliegt,44 [538] obwohl,januar [538] 000,afghanen [537] kabul,gro [537] oida_grantler,baerbock Top Word Pairs in Tweet in G6:
[508] abstimmung,#selbstbestimmungsgesetz [508] #selbstbestimmungsgesetz,bundestag [508] namentliche,abstimmung [508] bundestag,namentliche [508] hoffe,abstimmung [507] ainyrockstar,hoffe [507] abstimmung,abgeord [120] eigentlich,kritische [120] rede,bundestag [120] kritische,rede Top Word Pairs in Tweet in G7:
[100] arsch,breitsitzen [100] soziale,hängematte [100] bundestag,arsch [99] 16,bundestag [99] hängematte,nennen [99] breitsitzen,geld [99] arme,kinder [99] geld,arme [99] kinder,soziale [98] mauricehoefgen,16 Top Word Pairs in Tweet in G8:
[351] self,id [351] 2023,federal [351] 23,2023 [351] german,women [351] draft,bill [351] women,need [351] cabinet,passed [351] support,august [351] federal,cabinet [351] bill,sex Top Word Pairs in Tweet in G9:
[392] away,ready [392] head,of [392] enough,taurus [392] use,head [392] missiles,give [392] cruise,missiles [392] taurus,cruise [392] ready,use [341] part,away [341] give,part Top Word Pairs in Tweet in G10:
[7] fraktion,bundestag [7] deutschen,bundestag [5] #bundestag,#hessen [5] cdu,csu [4] deutscher,bundestag [4] #vereinigtestaatenvoneuropa,#europaparteien [4] afd,bundestag [4] #hessen,#bayernwahl [3] #bayernwahl,#länderfinanzausgleich [3] gesundheitsdaten,empfehlungen 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 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: