The graph represents a network of 4,508 Twitter users whose tweets in the requested range contained "ehealth", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 20 April 2020 at 06:28 UTC.
The requested start date was Monday, 20 April 2020 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 2-day, 13-hour, 28-minute period from Thursday, 16 April 2020 at 22:15 UTC to Sunday, 19 April 2020 at 11:43 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 : 4508
Unique Edges : 19692
Edges With Duplicates : 20161
Total Edges : 39853
Number of Edge Types : 5
Tweet : 363
Mentions : 28428
Replies to : 2802
Retweet : 1818
MentionsInRetweet : 6442
Self-Loops : 404
Reciprocated Vertex Pair Ratio : 0.0138279799296748
Reciprocated Edge Ratio : 0.0272787498538638
Connected Components : 176
Single-Vertex Connected Components : 59
Maximum Vertices in a Connected Component : 3983
Maximum Edges in a Connected Component : 38879
Maximum Geodesic Distance (Diameter) : 13
Average Geodesic Distance : 3.717134
Graph Density : 0.00126299639582635
Modularity : 0.290662
NodeXL Version : 1.0.1.431
Data Import : The graph represents a network of 4,508 Twitter users whose tweets in the requested range contained "ehealth", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 20 April 2020 at 06:28 UTC.
The requested start date was Monday, 20 April 2020 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 2-day, 13-hour, 28-minute period from Thursday, 16 April 2020 at 22:15 UTC to Sunday, 19 April 2020 at 11:43 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 : ehealth
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:
[2815] ncdcgov,fmohnigeria [2531] nighealthwatch,epidalert [1945] fmohnigeria,fmicnigeria [1909] ehealth_africa,africacdc [1900] whonigeria,ehealth_africa [1857] garshehu,drsanialiyu [1832] africacdc,dreoehanire [1828] fmicnigeria,whonigeria [1812] dreoehanire,garshehu [1628] drsanialiyu,nighealthwatch Top Word Pairs in Tweet in G1:
[1769] ncdcgov,fmohnigeria [1633] fmohnigeria,fmicnigeria [1569] ehealth_africa,africacdc [1565] whonigeria,ehealth_africa [1548] garshehu,drsanialiyu [1547] africacdc,dreoehanire [1545] fmicnigeria,whonigeria [1540] dreoehanire,garshehu [1540] nighealthwatch,epidalert [1379] drsanialiyu,nighealthwatch Top Word Pairs in Tweet in G2:
[74] enricomolinari,#ehealth [58] #ehealth,#health [57] health,care [55] transforming,health [53] #ehealth,#ai [53] #bigdata,#ehealth [49] 101blockchains,enricomolinari [47] ty,101blockchains [41] #doctors,#hospital [40] #hospital,#health Top Word Pairs in Tweet in G3:
[242] dear,realdonaldtrump [242] realdonaldtrump,wife [242] wife,fevered [242] fevered,confused [242] confused,coughing [242] coughing,two [242] two,weeks [242] weeks,finally [242] finally,found [242] found,ehealth Top Word Pairs in Tweet in G4:
[762] ncdcgov,fmohnigeria [759] nighealthwatch,epidalert [738] fmohnigeria,dreoehanire [738] dreoehanire,nighealthwatch [725] epidalert,ehealth_africa [715] ehealth_africa,whonigeria [710] whonigeria,africacdc [139] prince_daevid,ncdcgov [70] __klassy,prince_daevid [58] nwanyimanari,__klassy Top Word Pairs in Tweet in G5:
[7] #ehealth,#health [6] covid,19 [5] eu,toolbox [4] contact,tracing [4] ehealth,inc [3] ehealth,innovation [3] follow,alainbertrandmu [3] alainbertrandmu,aetherionmu [3] aetherionmu,ab_innov [3] ab_innov,let's Top Word Pairs in Tweet in G6:
[7] great,session [7] session,today [7] today,epic_ehealth [7] epic_ehealth,uniofplym [7] uniofplym,excited [7] excited,sw_ahsn [7] sw_ahsn,helping [7] helping,build [7] build,digital [7] digital,health Top Word Pairs in Tweet in G7:
[44] ehealth_africa,africacdc [42] whonigeria,ehealth_africa [41] ncdcgov,fmohnigeria [39] nighealthwatch,whonigeria [39] africacdc,epiafric [37] epiafric,epidalert [37] garshehu,drsanialiyu [35] fmohnigeria,dreoehanire [35] dreoehanire,nighealthwatch [33] epidalert,garshehu Top Word Pairs in Tweet in G8:
[10] 1st,virtual [10] virtual,video [10] video,visit [10] visit,patient [10] patient,otntelemedicine [10] otntelemedicine,#virtualcare [10] #virtualcare,researcher [10] researcher,wchwihv [10] ehealth,ontario [9] vess_stamenova,1st Top Word Pairs in Tweet in G9:
[5] covid,19 [3] lung,#ultrasound [3] #ultrasound,#covid_19 [3] #covid_19,covid [3] 19,pandemic [3] pandemic,recommendations [3] recommendations,#ehealth [3] #ehealth,banavarrete [3] banavarrete,efrasanchez [2] epocwiki,lung Top Word Pairs in Tweet in G10:
[62] ncdcgov,fmohnigeria [58] fmohnigeria,fmicnigeria [58] fmicnigeria,whonigeria [58] whonigeria,ehealth_africa [58] ehealth_africa,africacdc [58] africacdc,dreoehanire [58] dreoehanire,garshehu [57] nighealthwatch,epidalert [54] garshehu,drsanialiyu [53] drsanialiyu,nighealthwatch 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 G6:
Top Replied-To in G7:
Top Replied-To in G8:
Top Replied-To in G10:
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 Mentioned in G9:
Top Mentioned in G10:
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:
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Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: