The graph represents a network of 15,391 Twitter users whose recent tweets contained "#WEF lang:en", 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 Tuesday, 17 January 2023 at 20:04 UTC.
The tweets in the network were tweeted over the 2-day, 3-hour, 35-minute period from Sunday, 15 January 2023 at 15:31 UTC to Tuesday, 17 January 2023 at 19:07 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 : 15391
Unique Edges : 23686
Edges With Duplicates : 5792
Total Edges : 29478
Number of Edge Types : 5
Mentions : 3319
Replies to : 2196
Tweet : 2817
Retweet : 13471
MentionsInRetweet : 7675
Self-Loops : 2971
Reciprocated Vertex Pair Ratio : 0.00912099100627864
Reciprocated Edge Ratio : 0.0180771009374869
Connected Components : 1600
Single-Vertex Connected Components : 1078
Maximum Vertices in a Connected Component : 12656
Maximum Edges in a Connected Component : 26325
Maximum Geodesic Distance (Diameter) : 16
Average Geodesic Distance : 4.862461
Graph Density : 0.000100423236637497
Modularity : 0.721598
NodeXL Version : 1.0.1.508
Data Import : The graph represents a network of 15,391 Twitter users whose recent tweets contained "#WEF lang:en", 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 Tuesday, 17 January 2023 at 20:04 UTC.
The tweets in the network were tweeted over the 2-day, 3-hour, 35-minute period from Sunday, 15 January 2023 at 15:31 UTC to Tuesday, 17 January 2023 at 19:07 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 : #WEF lang:en
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 Color : In-Degree
Vertex Radius : In-Degree
Vertex Alpha : In-Degree
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[2670] world,economic [2650] economic,forum [1870] #wef,#klausschwab [1290] klaus,schwab [1241] #wef,#davos [1113] #wef23,#wef [1085] climate,change [845] private,jets [750] #davos,#wef [737] week,wef Top Word Pairs in Tweet in G1:
[1276] #wef,#klausschwab [476] sound,#wef [474] planning,cyber [474] cyber,attack [474] pandemic,sound [474] attack,pandemic [314] davos,2023 [313] 2023,#wef [311] world,domination [311] domination,quotes Top Word Pairs in Tweet in G2:
[409] world,economic [406] economic,forum [287] #wef,#davos2023 [272] davos,week [262] annual,meeting [257] private,jet [254] forum,annual [252] jet,motorcade [252] motorcade,discuss [252] highfalutin,elites Top Word Pairs in Tweet in G3:
[112] world,economic [112] #wef,#davos [106] economic,forum [76] #davos,#wef [42] #wef23,#wef [35] #wef,#wef23 [35] klaus,schwab [34] #wef,#wef2023 [31] annual,meeting [29] #worldeconomicforum,#wef Top Word Pairs in Tweet in G4:
[197] world,economic [197] economic,forum [159] #wef23,#wef [142] andrewlawton,wef [141] live,#davos [141] wef,#wef23royalrumble [141] #davos,andrewlawton [141] #wef23royalrumble,#wef23 [138] #wef,scam [136] american,politicians Top Word Pairs in Tweet in G5:
[439] #wef,#fauci [434] #ccp,#wef [431] #fauci,#vaccines [428] save,world [427] people,#davos [426] explaining,real [426] control,group [426] unvaccinated,save [426] #truth,#pfizer [426] #vaccines,resulting Top Word Pairs in Tweet in G6:
[388] #worldeconomicforum,#wef [328] #wef,#gates [267] #klausschwab,#worldeconomicforum [263] climate,change [262] like,breaks [262] #gates,#fauci [262] change,narrative [262] narrative,farting [262] show,things [262] ll,show Top Word Pairs in Tweet in G7:
[665] #wef,leaders [665] private,jets [664] leaders,stop [664] wef,shut [664] meat,climate [664] davos,event [664] thousands,private [664] shut,down [664] jets,flown [664] eating,meat Top Word Pairs in Tweet in G8:
[278] world,economic [275] economic,forum [268] #wef,#davos [249] #wef23,#wef [219] davos,hedera [213] gretathunberg,vanessa_vash [213] vanessa_vash,sumakhelena [212] #davos,#socialmedia [212] 30,influencers [212] #socialmedia,#smm Top Word Pairs in Tweet in G9:
[202] #wef,#wefpuppets [201] economic,forum [200] world,economic [178] #resistthewef,#wef [175] captured,countries [171] countries,world [171] fallen,#resistthewef [164] #wef,believe [159] nice,#resistthewef [159] notify,authorities Top Word Pairs in Tweet in G10:
[305] economic,forum [305] #wef,#davos [305] world,economic [298] #davos2023,#wef [298] forum,week [298] davos,klaus [298] schawb,evil [298] zelinsky,#davos2023 [298] klaus,schawb [298] plans,zelinsky Top Replied-To in Entire Graph:
Top Replied-To in G1:
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Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G10: