The graph represents a network of 7,110 Twitter users whose tweets in the requested range contained "@CDCgov OR @CDC_eHealth OR @CDCemergency", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 08 July 2022 at 19:59 UTC.
The requested start date was Friday, 08 July 2022 at 00:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 2-day, 7-hour, 40-minute period from Tuesday, 05 July 2022 at 16:20 UTC to Friday, 08 July 2022 at 00:01 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 : 7110
Unique Edges : 9091
Edges With Duplicates : 17241
Total Edges : 26332
Number of Edge Types : 5
Replies to : 2564
Mentions : 10594
MentionsInRetweet : 8644
Retweet : 4477
Tweet : 53
Self-Loops : 78
Reciprocated Vertex Pair Ratio : 0.0199100410107157
Reciprocated Edge Ratio : 0.0390427394772683
Connected Components : 2
Single-Vertex Connected Components : 0
Maximum Vertices in a Connected Component : 7108
Maximum Edges in a Connected Component : 26329
Maximum Geodesic Distance (Diameter) : 7
Average Geodesic Distance : 2.818051
Graph Density : 0.000305054961925999
Modularity : 0.41566
NodeXL Version : 1.0.1.449
Data Import : The graph represents a network of 7,110 Twitter users whose tweets in the requested range contained "@CDCgov OR @CDC_eHealth OR @CDCemergency", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 08 July 2022 at 19:59 UTC.
The requested start date was Friday, 08 July 2022 at 00:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 2-day, 7-hour, 40-minute period from Tuesday, 05 July 2022 at 16:20 UTC to Friday, 08 July 2022 at 00:01 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 : GraphServerTwitterSearch
Graph Term : @CDCgov OR @CDC_eHealth OR @CDCemergency
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:
[615] data,nejm [615] current,wave [613] long,isolate [613] isolate,data [613] nejm,half [613] half,patients [613] patients,stop [613] stop,shedding [613] shedding,#covid [613] #covid,current Top Word Pairs in Tweet in G1:
[326] cdcgov,everyone [323] hey,twitter [323] twitter,censors [323] censors,misleading [323] misleading,tweet [323] tweet,please [323] please,specific [323] specific,cdcgov [323] everyone,agree [322] alexberenson,hey Top Word Pairs in Tweet in G2:
[153] gross,negligence [153] negligence,reckless [153] reckless,endangerment [153] endangerment,health [153] health,life [153] life,happening [153] happening,hospitals [153] hospitals,throughout [152] calirunnerdoc,gross [152] throughout,coun Top Word Pairs in Tweet in G3:
[598] data,nejm [598] current,wave [596] long,isolate [596] isolate,data [596] nejm,half [596] half,patients [596] patients,stop [596] stop,shedding [596] shedding,#covid [596] #covid,current Top Word Pairs in Tweet in G4:
[161] erictopol,cdcgov [146] cdcgov,cdcmmwr [130] immmunocompromised,ic [130] ic,covid [130] covid,12 [130] 12,10 [130] 10,state [130] state,sample [130] sample,increased [130] increased,adverse Top Word Pairs in Tweet in G5:
[108] theboog9,cdcgov [108] well,tabletmag [108] tabletmag,best [108] best,lesliebienen [108] lesliebienen,discuss [108] discuss,depth [108] depth,problems [108] problems,recent [108] recent,infant [108] infant,toddler Top Word Pairs in Tweet in G6:
[39] 24,yes [39] yes,love [39] love,cdcgov [39] cdcgov,criticize [39] criticize,care [39] care,damn [39] damn,comms [38] drericding,24 [38] comms,strai [29] drericding,cdcgov Top Word Pairs in Tweet in G7:
[77] cdcgov,pfizer [76] side,effects [73] aching,pain [73] pain,chest [73] chest,over [73] over,more [73] more,temporary [73] temporary,mild [73] mild,side [73] effects,isn Top Word Pairs in Tweet in G8:
[114] forms,#dementia [113] know,reduce [113] reduce,risk [113] risk,#alzheimers [113] #alzheimers,disease [113] disease,related [113] related,forms [113] #dementia,example [113] example,making [112] lead_coalition,know Top Word Pairs in Tweet in G9:
[23] ebennett74,cdcgov [20] cdcgov,continue [20] continue,deny [20] deny,efficacy [20] efficacy,natural [20] natural,immunity [20] immunity,covid [20] covid,exam [20] exam,room [20] room,doctor Top Word Pairs in Tweet in G10:
[33] covid,lies [32] one,called [32] called,more [32] more,covid [32] lies,reporter [32] reporter,daniel [32] daniel,horowitz [32] horowitz,blaze [32] blaze,suppose [32] suppose,reason Top Replied-To in Entire Graph:
Top Replied-To in G1:
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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:
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Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G10: