The graph represents a network of 14,445 Twitter users whose recent tweets contained "‘ecigarette’ OR ‘e-cig' OR ‘ecig’ OR 'vaping'", 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 Wednesday, 13 November 2019 at 13:57 UTC.
The tweets in the network were tweeted over the 22-hour, 18-minute period from Tuesday, 12 November 2019 at 14:49 UTC to Wednesday, 13 November 2019 at 13:08 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 : 14445
Unique Edges : 21417
Edges With Duplicates : 6091
Total Edges : 27508
Self-Loops : 5263
Reciprocated Vertex Pair Ratio : 0.0138910485668377
Reciprocated Edge Ratio : 0.02740146209294
Connected Components : 3859
Single-Vertex Connected Components : 2762
Maximum Vertices in a Connected Component : 7906
Maximum Edges in a Connected Component : 20236
Maximum Geodesic Distance (Diameter) : 20
Average Geodesic Distance : 5.217986
Graph Density : 9.37531842580539E-05
Modularity : 0.620954
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 14,445 Twitter users whose recent tweets contained "‘ecigarette’ OR ‘e-cig' OR ‘ecig’ OR 'vaping'", 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 Wednesday, 13 November 2019 at 13:57 UTC.
The tweets in the network were tweeted over the 22-hour, 18-minute period from Tuesday, 12 November 2019 at 14:49 UTC to Wednesday, 13 November 2019 at 13:08 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 : ‘ecigarette’ OR ‘e-cig' OR ‘ecig’ OR 'vaping'
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:
[2765] lung,transplant [2653] double,lung [1322] e,cigarettes [994] first,double [990] damaged,vaping [919] whose,lungs [917] e,cigarette [811] vaping,related [793] lungs,damaged [736] vaping,products Top Word Pairs in Tweet in G1:
[593] lung,transplant [556] double,lung [216] gets,double [175] vaping,related [158] injury,gets [154] vaping,injury [153] certain,death [148] facing,certain [121] vaping,products [114] transplant,vaping Top Word Pairs in Tweet in G2:
[469] e,cigarettes [290] editorial,board [285] lung,transplant [275] nicotine,vaping [271] double,lung [257] vaping,products [221] vitamin,e [217] e,cigarette [206] public,health [202] e,acetate Top Word Pairs in Tweet in G3:
[424] vaping,industry [363] meeting,representatives [363] e,cigarette [362] representatives,vaping [362] come,up [362] children,s [362] s,health [361] industry,together [361] together,medical [361] medical,professionals Top Word Pairs in Tweet in G4:
[512] double,lung [512] lung,transplant [380] damaged,vaping [379] lungs,damaged [375] whose,lungs [372] first,double [371] u,s [369] transplant,u [200] teenager,whose [194] doctors,detroit Top Word Pairs in Tweet in G5:
[93] warn,vaping [88] cardiologists,warn [88] vaping,dangerous [88] dangerous,addictive [87] addictive,banned [69] allergic,reaction [65] blood,vessels [63] e,cigarettes [42] damage,brain [42] brain,heart Top Word Pairs in Tweet in G6:
[294] lung,transplant [275] double,lung [179] first,double [165] henry,ford [158] whose,lungs [154] damaged,vaping [153] patient,whose [135] performed,believe [116] detroit,performed [114] health,system Top Word Pairs in Tweet in G7:
[298] e,cigarettes [279] cigarettes,more [278] more,harmful [278] harmful,smoker [278] smoker,s [278] cigarettes,according [278] according,new [278] new,study [276] s,heart [276] heart,traditional Top Word Pairs in Tweet in G8:
[236] seen,before [213] nothing,ve [213] ve,seen [212] saw,lungs [212] lungs,nothing [212] before,ve [212] ve,doing [212] doing,lung [212] lung,transplants [212] transplants,20 Top Word Pairs in Tweet in G9:
[212] 000,cases [107] lung,cancer [107] cases,vaping [106] over,250 [106] 250,000 [106] cases,lung [106] cancer,year [106] year,united [106] united,states [106] states,alone Top Word Pairs in Tweet in G10:
[193] fahrenthold,now [193] now,good [193] good,time [193] time,remind [193] remind,people [193] people,piece [193] piece,washington [193] washington,post [193] post,ran [193] ran,presidential Top Replied-To in Entire Graph:
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Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
Top Tweeters in G2:
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Top Tweeters in G10: