The graph represents a network of 2,851 Twitter users whose recent tweets contained "#eaad OR #eaad2019 OR #eaad19 OR #waaw OR #waaw19 OR #waaw2019 OR #antibioticawareness OR #worldantibioticawarenessweek OR #antibioticawarenessweek OR "antibiotic awareness week" OR "antibiotic awareness day" OR #stopsuperbugs OR #aaw2019 OR #aaw19 OR #antibioticguardian OR #keepantibioticsworking OR #antibioticresistance since:2019-11-24", 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 Monday, 25 November 2019 at 13:03 UTC.
The tweets in the network were tweeted over the 1-day, 12-hour, 56-minute period from Sunday, 24 November 2019 at 00:00 UTC to Monday, 25 November 2019 at 12:56 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 : 2851
Unique Edges : 5327
Edges With Duplicates : 3194
Total Edges : 8521
Self-Loops : 625
Reciprocated Vertex Pair Ratio : 0.0133680555555556
Reciprocated Edge Ratio : 0.0263834161384273
Connected Components : 343
Single-Vertex Connected Components : 118
Maximum Vertices in a Connected Component : 1703
Maximum Edges in a Connected Component : 7030
Maximum Geodesic Distance (Diameter) : 12
Average Geodesic Distance : 4.601363
Graph Density : 0.000718369054871482
Modularity : 0.5718
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 2,851 Twitter users whose recent tweets contained "#eaad OR #eaad2019 OR #eaad19 OR #waaw OR #waaw19 OR #waaw2019 OR #antibioticawareness OR #worldantibioticawarenessweek OR #antibioticawarenessweek OR "antibiotic awareness week" OR "antibiotic awareness day" OR #stopsuperbugs OR #aaw2019 OR #aaw19 OR #antibioticguardian OR #keepantibioticsworking OR #antibioticresistance since:2019-11-24", 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 Monday, 25 November 2019 at 13:03 UTC.
The tweets in the network were tweeted over the 1-day, 12-hour, 56-minute period from Sunday, 24 November 2019 at 00:00 UTC to Monday, 25 November 2019 at 12:56 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 : #eaad OR #eaad2019 OR #eaad19 OR #waaw OR #waaw19 OR #waaw2019 OR #antibioticawareness OR #worldantibioticawarenessweek OR #antibioticawarenessweek OR "antibiotic awareness week" OR "antibiotic awareness day" OR #stopsuperbugs OR #aaw2019 OR #aaw19 OR #antibioticguardian OR #keepantibioticsworking OR #antibioticresistance since:2019-11-24
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:
[457] awareness,week [355] antibiotic,awareness [348] future,#antibiotics [345] #antibiotics,depends [282] world,antibiotic [248] who_europe,whosearo [248] whosearo,pahowho [248] pahowho,whoemro [248] whoemro,whowpro [248] whowpro,whoafro Top Word Pairs in Tweet in G1:
[68] awareness,week [46] antibiotic,awareness [45] antibiotic,resistance [37] world,antibiotic [35] use,antibiotics [28] antimicrobial,resistance [26] #waaw2019,#handleantibioticswithcare [25] successful,#waaw2019 [25] kenya,national [25] national,theatre Top Word Pairs in Tweet in G2:
[225] who_europe,whosearo [225] whosearo,pahowho [225] pahowho,whoemro [225] whoemro,whowpro [225] whowpro,whoafro [225] whoafro,drtedros [225] drtedros,un_news_centre [211] future,#antibiotics [210] #antibiotics,depends [171] prevents,infections Top Word Pairs in Tweet in G3:
[193] supporting,campaignplea [193] #waaw2019,#unitedagainstdrugresistance [156] awareness,#waaw2019 [156] #unitedagainstdrugresistance,supporting [155] spreading,awareness [155] campaignplea,icmrdelhi [116] awareness,week [111] future,#antibiotics [109] #antibiotics,depends [96] rise,#antibioticresistance Top Word Pairs in Tweet in G4:
[25] antibiotic,awareness [25] awareness,week [22] antibiotic,resistance [21] world,antibiotic [8] taking,antibiotics [7] don,t [7] #beantibioticsaware,#antibioticawarenessweek [7] #antibioticawarenessweek,antibiotic [7] antibiotic,best [7] best,matches Top Word Pairs in Tweet in G5:
[27] antibiotic,resistance [18] firmado,grupo [18] grupo,infecciones [18] infecciones,grupo [18] grupo,#pacientes [18] #pacientes,#antibioticos [18] #antibioticos,#eaad2019 [18] #eaad2019,#pranamr2019 [18] #pranamr2019,#keppantibioiticsworking [16] adaptación,infografía Top Word Pairs in Tweet in G6:
[25] use,#antibiotics [23] candy,use [21] awareness,week [21] #antibiotics,appropriately [21] appropriately,join [21] informing,uchceu [21] uchceu,campus [21] emmaalos,karinasanchez20 [21] karinasanchez20,marbenlloch [21] marbenlloch,marta_vallesn Top Word Pairs in Tweet in G7:
[20] drug,resistant [20] northeastern,researchers [16] #amr,#superbugs [16] #superbugs,#antibioticresistance [15] resistant,superbugs [14] very,exciting [14] exciting,news [14] news,kim [14] kim,lewis's [14] lewis's,lab Top Word Pairs in Tweet in G8:
[8] use,#antibiotics [7] wellcome_amr,reactgroup [7] reactgroup,azr86 [7] azr86,idsainfo [7] idsainfo,hj_chapman [7] hj_chapman,mariamparwaiz [7] mariamparwaiz,hboucher3 [7] hboucher3,helennewland [6] address,#amr [5] find,out Top Word Pairs in Tweet in G9:
[39] taking,antibiotics [39] antibiotics,don [39] don,t [39] t,need [39] need,puts [39] puts,family [39] family,risk [39] risk,always [39] always,take [39] take,doctor Top Word Pairs in Tweet in G10:
[37] cdc,report [37] report,suggests [37] suggests,antibacterial [37] antibacterial,resistance [37] resistance,undo [37] undo,century [37] century,progress [37] progress,#antibacterial [37] #antibacterial,#antimicrobialresistance [37] #antimicrobialresistance,#antibioticresistance 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 G5:
Top Replied-To in G7:
Top Replied-To in G8:
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 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: