The graph represents a network of 389 Twitter users whose recent tweets contained "#NAAW2019", 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 12:09 UTC.
The tweets in the network were tweeted over the 7-day, 4-hour, 45-minute period from Saturday, 16 November 2019 at 13:45 UTC to Saturday, 23 November 2019 at 18:31 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 : 389
Unique Edges : 713
Edges With Duplicates : 826
Total Edges : 1539
Self-Loops : 85
Reciprocated Vertex Pair Ratio : 0.0344418052256532
Reciprocated Edge Ratio : 0.0665901262916188
Connected Components : 13
Single-Vertex Connected Components : 4
Maximum Vertices in a Connected Component : 353
Maximum Edges in a Connected Component : 1496
Maximum Geodesic Distance (Diameter) : 6
Average Geodesic Distance : 2.575951
Graph Density : 0.00577081069620756
Modularity : 0.352593
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 389 Twitter users whose recent tweets contained "#NAAW2019", 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 12:09 UTC.
The tweets in the network were tweeted over the 7-day, 4-hour, 45-minute period from Saturday, 16 November 2019 at 13:45 UTC to Saturday, 23 November 2019 at 18:31 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 : #NAAW2019
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:
[308] #naaw2019,#amractionng [300] #amractionng,#waaw2019 [275] #amractionng,#naaw2019 [245] #naaw2019,#waaw2019 [196] #waaw2019,#antibioticguardian [157] #antibioticguardian,#naaw2019 [126] #africawaaw,#amractionng [115] responsible,use [115] use,antibiotics [110] awareness,week Top Word Pairs in Tweet in G1:
[153] #naaw2019,#amractionng [152] #amractionng,#waaw2019 [98] #amractionng,#naaw2019 [91] #antibioticguardian,#naaw2019 [87] #waaw2019,#antibioticguardian [79] use,antibiotics [78] #naaw2019,#waaw2019 [77] responsible,use [77] role,play [61] urgent,global Top Word Pairs in Tweet in G2:
[116] #amractionng,#naaw2019 [110] #naaw2019,#waaw2019 [87] #africawaaw,#amractionng [73] #waaw2019,#antibioticguardian [50] #naaw2019,#amractionng [45] #amractionng,#waaw2019 [27] hcsmafrica,t [26] #antibioticguardian,#naaw2019 [18] hcsmafrica,_africanunion [18] _africanunion,africacdc Top Word Pairs in Tweet in G3:
[87] #naaw2019,#amractionng [56] #amractionng,#waaw2019 [44] #amractionng,#naaw2019 [41] #naaw2019,#waaw2019 [32] #antibioticguardian,#naaw2019 [25] #waaw2019,#antibioticguardian [22] #amractionng,#handleantibioticswithcare [21] handle,antibiotics [21] antibiotics,care [21] viral,infections Top Word Pairs in Tweet in G4:
[27] awareness,week [24] #waaw2019,#naaw2019 [20] antibiotics,awareness [19] s,world [19] world,antibiotics [19] week,two [19] two,simple [19] simple,questions [19] questions,nhw [19] nhw,engage Top Word Pairs in Tweet in G5:
[36] #amractionng,#waaw2019 [30] awareness,week [30] #waaw2019,#naaw2019 [29] next,week [29] week,exciting [29] exciting,line [29] line,up [29] up,activities [29] activities,antibiotic [29] antibiotic,awareness Top Word Pairs in Tweet in G6:
[2] drcjlittle,bradspellberg [2] bradspellberg,idstewardship [2] idstewardship,drdianeashiru [2] drdianeashiru,elizbeech [2] elizbeech,absteward [2] absteward,1antruk [2] 1antruk,jacindaardern [2] jacindaardern,cdcgov [2] cdcgov,brxad [2] brxad,voxdotcom Top Word Pairs in Tweet in G8:
[9] #waaw2019,#naaw2019 [6] bacteria,becomes [6] becomes,resistant [6] resistant,antibiotic [6] antibiotic,common [6] common,infections [6] infections,becomes [6] becomes,untreatable [6] untreatable,leading [6] leading,longer Top Word Pairs in Tweet in G9:
[2] ahs,livestream [2] livestream,nov22 [2] nov22,9 [2] 9,12am [2] 12am,#naaw2019 [2] #naaw2019,highlighting [2] highlighting,program [2] program,models [2] models,ahs_yyczone [2] ahs_yyczone,ahs_media Top Replied-To in Entire Graph:
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Top Replied-To in G3:
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Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
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Top Mentioned in G9:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G10: