The graph represents a network of 2,717 Twitter users whose tweets in the requested range contained "hemophilia OR haemophilia OR bleedingdisorders OR hemochat ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 18 September 2019 at 14:30 UTC.
The requested start date was Monday, 16 September 2019 at 00:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 5,000.
The tweets in the network were tweeted over the 13-day, 21-hour, 54-minute period from Monday, 02 September 2019 at 01:26 UTC to Sunday, 15 September 2019 at 23:21 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 : 2717
Unique Edges : 3081
Edges With Duplicates : 1739
Total Edges : 4820
Number of Edge Types : 3
Tweet : 1306
Mentions : 3252
Replies to : 262
Self-Loops : 1306
Reciprocated Vertex Pair Ratio : 0.0231985940246046
Reciprocated Edge Ratio : 0.0453452421848162
Connected Components : 503
Single-Vertex Connected Components : 305
Maximum Vertices in a Connected Component : 605
Maximum Edges in a Connected Component : 1683
Maximum Geodesic Distance (Diameter) : 11
Average Geodesic Distance : 3.697082
Graph Density : 0.00039447801249212
Modularity : 0.607102
NodeXL Version : 1.0.1.419
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[382] smhs,hospital [271] factor,viii [269] patients,families [268] few,patients [267] day,care [267] care,centre [265] centre,smhs [264] families,made [264] made,difficult [264] difficult,trek Top Word Pairs in Tweet in G1:
[350] smhs,hospital [258] even,very [258] very,few [258] few,patients [258] patients,families [258] families,made [258] made,difficult [258] difficult,trek [258] trek,day [258] day,care Top Word Pairs in Tweet in G2:
[33] times,more [30] three,times [24] more,prevalent [23] hemophilia,three [18] study,finds [17] gene,therapy [17] more,common [17] thought,study [17] prevalent,thought [16] hemophilia,b Top Word Pairs in Tweet in G3:
[98] gene,therapy [40] wfhemophilia,wfh [31] prevalence,#hemophilia [25] spearheaded,scientific [25] scientific,study [25] study,updating [25] updating,prevalence [25] higher,numbers [25] numbers,previously [24] wfh,spearheaded Top Word Pairs in Tweet in G4:
[250] 1980s,bayer [250] bayer,co [250] co,manufactured [250] manufactured,sold [250] sold,factor [250] factor,viii [250] viii,hemophilia [250] hemophilia,clotting [250] clotting,medicine [250] medicine,hiv Top Word Pairs in Tweet in G5:
[64] #x1,#엑스원 [56] #조승연,#승연 [35] ㅋㅋㅋㅋㅋ,ㅋㅋㅋㅋㅋ [34] #조승연,#x1 [30] #엑스원,#조승연 [30] #승연,#choseungyoun [19] #조승연,#choseungyoun [18] ㅠ,ㅠ [17] #조승연,#남도현 [16] #승연,x1members Top Word Pairs in Tweet in G6:
[27] contaminated,blood [27] blood,scandal [23] scandal,anger [23] anger,over [23] over,key [23] key,department [23] department,health [23] health,documents [23] documents,marked [23] government,auditor Top Word Pairs in Tweet in G7:
[3] rjdownard,hauxton [3] watcheronawall,andrewrchapman [3] ebatterson,okmkmkok [3] okmkmkok,standbackup2 [3] wisemanryder,rosarubicon [3] rosarubicon,odktiger [3] odktiger,bellpipe41 [3] bellpipe41,hubie0 [3] hubie0,atheistbigfoot [3] atheistbigfoot,ofrewol Top Word Pairs in Tweet in G8:
[54] three,year [54] year,old [54] old,boy [54] boy,haemophilia [54] haemophilia,acquired [54] acquired,brain [54] brain,injury [54] injury,facing [54] facing,removal [54] removal,country Top Replied-To in Entire Graph:
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Top Mentioned in Entire Graph:
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Top Tweeters in Entire Graph:
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