The graph represents a network of 7,702 Twitter users whose tweets in the requested range contained "NCoV", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 29 October 2020 at 10:57 UTC.
The requested start date was Thursday, 29 October 2020 at 00:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 2-day, 12-hour, 55-minute period from Monday, 26 October 2020 at 11:05 UTC to Thursday, 29 October 2020 at 00:00 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 : 7702
Unique Edges : 3494
Edges With Duplicates : 11187
Total Edges : 14681
Number of Edge Types : 5
Tweet : 1665
Replies to : 811
Mentions : 1672
Retweet : 4943
MentionsInRetweet : 5590
Self-Loops : 1701
Reciprocated Vertex Pair Ratio : 0.00409778154585276
Reciprocated Edge Ratio : 0.00816211652124965
Connected Components : 1422
Single-Vertex Connected Components : 655
Maximum Vertices in a Connected Component : 3390
Maximum Edges in a Connected Component : 8223
Maximum Geodesic Distance (Diameter) : 19
Average Geodesic Distance : 5.742637
Graph Density : 0.000119804895721016
Modularity : 0.49911
NodeXL Version : 1.0.1.441
Data Import : The graph represents a network of 7,702 Twitter users whose tweets in the requested range contained "NCoV", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 29 October 2020 at 10:57 UTC.
The requested start date was Thursday, 29 October 2020 at 00:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 2-day, 12-hour, 55-minute period from Monday, 26 October 2020 at 11:05 UTC to Thursday, 29 October 2020 at 00:00 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 : NCoV
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:
[1144] covid,19 [468] 2019,ncov [393] 3月26日の航空機内で発生した集団感染の事例です,どうして今頃このような重要な事例報告がされるのだろうか [393] どうして今頃このような重要な事例報告がされるのだろうか,尾身茂の [393] 尾身茂の,旅行自体が感染を起こすことはない [393] 旅行自体が感染を起こすことはない,発言は7月だったわけですが [392] derive_ip,3月26日の航空機内で発生した集団感染の事例です [392] 発言は7月だったわけですが,３月の時点ですでにこのような重要な事例が国内でも発生していたということ [343] protect,yourself [327] ways,help Top Word Pairs in Tweet in G1:
[267] covid,19 [204] higher,risk [204] risk,severe [201] severe,#covid19 [201] #covid19,illness [199] steps,take [198] illness,learn [198] learn,steps [196] help,slow [182] different,year Top Word Pairs in Tweet in G2:
[227] covid,19 [188] 2019,ncov [148] covid,cases [145] cases,increase [145] ncov,recombinomics [145] recombinomics,inc [43] trick,treating [41] halloween,activities [36] learn,more [25] wear,mask Top Word Pairs in Tweet in G3:
[383] 3月26日の航空機内で発生した集団感染の事例です,どうして今頃このような重要な事例報告がされるのだろうか [383] どうして今頃このような重要な事例報告がされるのだろうか,尾身茂の [383] 尾身茂の,旅行自体が感染を起こすことはない [383] 旅行自体が感染を起こすことはない,発言は7月だったわけですが [382] derive_ip,3月26日の航空機内で発生した集団感染の事例です [382] 発言は7月だったわけですが,３月の時点ですでにこのような重要な事例が国内でも発生していたということ [5] 2020,12 [5] covid,19 [4] public,health [4] ウイルス排出量の多い人がマスクなしで搭乗すると,スーパースプレッダーになるのかも Top Word Pairs in Tweet in G4:
[174] datos,#covid19 [96] actualización,datos [96] información,coronavirus [93] sanidadgob,actualización [78] #covid19,españa [78] españa,primer [78] primer,caso [78] caso,inicial [78] inicial,actualizados [78] actualizados,hoy Top Word Pairs in Tweet in G5:
[136] ways,help [128] one,best [124] make,sure [123] protect,yourself [120] help,protect [120] #covid19,make [119] mask,one [119] best,ways [119] yourself,others [119] others,getting Top Word Pairs in Tweet in G6:
[97] covid,19 [88] funcionen,quarantenes [88] què,fer [82] vostre,cas [81] cas,individual [81] saber,què [79] quarantenes,covid [78] 19,aquesta [78] aquesta,guia [78] servirà,detectar Top Word Pairs in Tweet in G7:
[8] covid,19 [6] 2019,ncov [4] hong,kong [4] tony,chung [4] hkrassenstein,realdonaldtrump [4] during,week [4] 20,49 [4] 50,69 [3] seek,asylum [3] cdc,website Top Word Pairs in Tweet in G8:
[155] ministerio,salud [139] hilo,balance [139] balance,diario [139] diario,#covid_19 [139] octubre,ministerio [139] salud,informa [139] informa,casos [139] casos,totales [136] ministeriosalud,hilo [72] #covid_19,miércoles Top Word Pairs in Tweet in G9:
[91] germany,holding [91] holding,tough [91] tough,line [91] line,14 [91] 14,day [91] day,rate [91] rate,map [91] map,nordic [91] nordic,countries [90] mackayim,germany Top Word Pairs in Tweet in G10:
[134] 10,00 [84] #covid19,#covid19at [80] neuwirthe,#covid19 [67] 27,10 [67] 00,gibt [67] neue,positiv [67] positiv,getestete [67] getestete,fälle [67] fälle,datenquelle [59] heute,28 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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