The graph represents a network of 2,828 Twitter users whose recent tweets contained "Krankenversicherung", 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, 22 February 2023 at 16:00 UTC.
The tweets in the network were tweeted over the 10-day, 2-hour, 55-minute period from Sunday, 12 February 2023 at 13:00 UTC to Wednesday, 22 February 2023 at 15:56 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 : 2828
Unique Edges : 3300
Edges With Duplicates : 175
Total Edges : 3475
Number of Edge Types : 5
Retweet : 1760
Replies to : 639
Mentions : 528
MentionsInRetweet : 193
Tweet : 355
Self-Loops : 360
Reciprocated Vertex Pair Ratio : 0.00825354902608121
Reciprocated Edge Ratio : 0.0163719711853307
Connected Components : 459
Single-Vertex Connected Components : 209
Maximum Vertices in a Connected Component : 1708
Maximum Edges in a Connected Component : 2403
Maximum Geodesic Distance (Diameter) : 14
Average Geodesic Distance : 4.78582
Graph Density : 0.000382000401263028
Modularity : 0.777457
NodeXL Version : 1.0.1.510
Data Import : The graph represents a network of 2,828 Twitter users whose recent tweets contained "Krankenversicherung", 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, 22 February 2023 at 16:00 UTC.
The tweets in the network were tweeted over the 10-day, 2-hour, 55-minute period from Sunday, 12 February 2023 at 13:00 UTC to Wednesday, 22 February 2023 at 15:56 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 : Krankenversicherung
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:
[767] 2000,euro [521] millionen,flüchtlinge [520] beteiligen,genau [520] krankenversicherung,aufnehmen [520] aufnehmen,sozialkassen [520] wundern,krankensystem [520] überfordert,2000 [520] flüchtlinge,krankenversicherung [520] euro,kosten [520] krankensystem,finanziell Top Word Pairs in Tweet in G1:
[454] 2000,euro [453] kosten,beteiligen [453] flüchtlinge,krankenversicherung [453] beteiligen,genau [453] krankenversicherung,aufnehmen [453] eingezahlt,wundern [453] sozialkassen,eingezahlt [453] krankensystem,finanziell [453] euro,kosten [453] aufnehmen,sozialkassen Top Word Pairs in Tweet in G2:
[140] eingemachte,#raffelhüschen [140] gehts,ans [140] absolute,sozialstaats [140] selbstbeteiligung,2000 [140] eingeleitet,#ampelkoalition [140] fordert,selbstbeteiligung [140] ans,eingemachte [140] arztbesuch,rechnung [140] krankenversicherten,arztbesuch [140] gesetzlich,krankenversicherten Top Word Pairs in Tweet in G3:
[20] 2000,euro [17] euro,selbstbeteiligung [14] private,krankenversicherung [12] fordert,2000 [12] gesetzlichen,krankenversicherung [9] selbstbeteiligung,kassenpatienten [9] bernd,raffelhüschen [8] ökonom,raffelhüschen [7] defizit,17 [7] milliarden,euro Top Word Pairs in Tweet in G4:
[142] 2000,euro [120] ökonom,raffelhüschen [120] raffelhüschen,fordert [120] fordert,2000 [120] euro,selbstbeteiligung [117] selbstbeteiligung,kassenpatienten [108] kassenpatienten,welt [105] leisten,ökonom [105] system,leisten [88] kollabieren,system Top Word Pairs in Tweet in G5:
[6] bmg_bund,karl_lauterbach [6] womöglich,einbezahlt [6] c_lindner,diepressecom [6] leistung,zuzahlung [6] leben,womöglich [6] gleiche,leistung [6] einbezahlt,bekommen [6] bekommen,gleiche [6] eigentlich,krankenversicherung [6] sozialleistungen,leben Top Word Pairs in Tweet in G6:
[58] reicht,privatisiert [58] durchschnittsverdienst,2021 [58] 000,drecksladen [58] 2021,100 [58] drecksladen,reicht [58] zahlt,jährlich [58] gesetzliche,krankenversicherung [58] 000,gesetzliche [58] krankenversicherung,000 [58] 100,brutto Top Word Pairs in Tweet in G7:
[47] private,krankenversicherung [47] rente,zeche [47] zeche,politischen [47] inflation,rente [47] system,geschwächt [47] politischen,fehler [47] krankenversicherung,gesetzliche [47] energiepolitik,migrationskrise [47] stets,gleiche [47] fehler,zahlt Top Word Pairs in Tweet in G8:
[37] kassenpatienten,2000 [37] masken,verdammt [37] 2000,euro [37] fdp,läuft [37] teuer,kassenpatienten [37] bezahlen,spiegel [37] alaaf,masken [37] krankenversicherung,fdp [37] kölle,alaaf [37] verdammt,teuer Top Word Pairs in Tweet in G9:
[7] gesetzliche,krankenversicherung [6] brd,wochen [6] umsonst,uniausbildung [6] derartig,naivitaet [6] lachen,schnell [6] krankenversicherung,umsonst [6] wochen,urlaub [6] ljoliik,ueber [6] urlaub,krankenversicherung [6] leben,brd Top Word Pairs in Tweet in G10:
[36] zerstört,#eigenbeteiligung [36] deutschland,gesundheitssystem [36] unverschämter,angriff [36] #krankenversicherung,weiterer [36] weiterer,unverschämter [36] angriff,sozialsysteme [36] unsinn,zerstört [36] dinge,deutschland [36] sozialsysteme,widerwärtig [36] hanebüchenem,unsinn Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
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 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: