The graph represents a network of 379 Twitter users whose recent tweets contained "#AfricaWAAW", 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 12:03 UTC.
The tweets in the network were tweeted over the 8-day, 22-hour, 44-minute period from Saturday, 16 November 2019 at 07:23 UTC to Monday, 25 November 2019 at 06:07 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 : 379
Unique Edges : 1058
Edges With Duplicates : 3503
Total Edges : 4561
Self-Loops : 183
Reciprocated Vertex Pair Ratio : 0.0875420875420875
Reciprocated Edge Ratio : 0.160990712074303
Connected Components : 1
Single-Vertex Connected Components : 0
Maximum Vertices in a Connected Component : 379
Maximum Edges in a Connected Component : 4561
Maximum Geodesic Distance (Diameter) : 6
Average Geodesic Distance : 2.609199
Graph Density : 0.0112730521701498
Modularity : 0.155995
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 379 Twitter users whose recent tweets contained "#AfricaWAAW", 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 12:03 UTC.
The tweets in the network were tweeted over the 8-day, 22-hour, 44-minute period from Saturday, 16 November 2019 at 07:23 UTC to Monday, 25 November 2019 at 06:07 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 : #AfricaWAAW
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:
[151] #amractionng,#naaw2019 [142] #naaw2019,#waaw2019 [136] #africawaaw,#amractionng [126] antibiotic,resistance [103] #africawaaw,drjayvarma [92] #waaw2019,#antibioticguardian [90] hashtag,#africawaaw [89] twitter,chat [74] awareness,week [70] antimicrobial,resistance Top Word Pairs in Tweet in G1:
[44] #africawaaw,drjayvarma [25] r,t [23] t4,1 [22] wash,hands [21] hashtag,#africawaaw [20] 8pm,eat [20] twitter,chat [19] antibiotic,resistance [17] hcsmafrica,t2 [17] #africawaaw,mirfinm Top Word Pairs in Tweet in G2:
[42] #waaw19,#africawaaw [36] #waaw2019,#africawaaw [31] #africawaaw,#antibioticguardian [18] #antibioticguardian,#stopsuperbugsafrica [12] les,antibiotiques [11] #antibioticguardian,#antibioticresistance [10] #antibioticresistance,#amresistancefighter [10] il,faut [10] lutte,contre [10] #africawaaw,#amractionng Top Word Pairs in Tweet in G3:
[63] #amractionng,#naaw2019 [60] #naaw2019,#waaw2019 [59] #africawaaw,#amractionng [45] #waaw2019,#antibioticguardian [40] antibiotic,resistance [32] #africawaaw,drjayvarma [28] africacdc,drjayvarma [28] hashtag,#africawaaw [28] #antibioticresistance,#africawaaw [26] antimicrobial,resistance Top Word Pairs in Tweet in G4:
[21] antibiotic,resistance [21] hashtag,#africawaaw [18] #africawaaw,chat [16] awareness,week [15] antimicrobial,resistance [14] twitter,chat [14] #stopsuperbugsafrica,#antibioticguardian [14] join,#africawaaw [13] share,views [12] 8pm,eat Top Word Pairs in Tweet in G5:
[18] #africawaaw,#stopsuperbugsafrica [17] awareness,week [16] whoafro,faoafrica [16] faoafrica,oieanimalhealth [15] #amractionng,#naaw2019 [15] #naaw2019,#waaw2019 [14] africacdc,au_ibar [14] #africawaaw,drjayvarma [14] _africanunion,africacdc [14] #africawaaw,#amractionng Top Word Pairs in Tweet in G6:
[45] #amractionng,#naaw2019 [43] #naaw2019,#waaw2019 [32] #africawaaw,#amractionng [23] antibiotic,resistance [21] #waaw2019,#antibioticguardian [19] one,urgent [19] urgent,global [19] global,health [19] health,threats [19] threats,everyone Top Word Pairs in Tweet in G7:
[12] drdianeashiru,olaoyeomotayo [10] #africawaaw,#amractionng [9] become,#antibioticguardian [9] cw_pharmacists,drdianeashiru [8] #africawaaw,#waaw2019 [8] twitter,chat [8] #antibioticresistance,#antimicrobialresistance [7] cc,wande_a [7] #waaw2019,#africawaaw [7] africacdc,hcsmafrica Top Word Pairs in Tweet in G8:
[7] #africawaaw,#hcsmsa [5] join,#protectantibioticsza [5] #protectantibioticsza,twitter [5] twitter,chat [5] chat,november [5] november,20th [5] 20th,everyone [5] everyone,welcome [5] welcome,check [5] check,here 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 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: