The graph represents a network of 10,265 Twitter users whose recent tweets contained "#HIV", 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 Friday, 29 July 2022 at 23:16 UTC.
The tweets in the network were tweeted over the 9-day, 1-hour, 6-minute period from Wednesday, 20 July 2022 at 20:23 UTC to Friday, 29 July 2022 at 21:30 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 : 10265
Unique Edges : 23726
Edges With Duplicates : 14249
Total Edges : 37975
Number of Edge Types : 5
Retweet : 13259
MentionsInRetweet : 16379
Tweet : 2841
Mentions : 4952
Replies to : 544
Self-Loops : 3018
Reciprocated Vertex Pair Ratio : 0.0287741885446155
Reciprocated Edge Ratio : 0.0559387839722568
Connected Components : 754
Single-Vertex Connected Components : 421
Maximum Vertices in a Connected Component : 8661
Maximum Edges in a Connected Component : 36136
Maximum Geodesic Distance (Diameter) : 10
Average Geodesic Distance : 3.742402
Graph Density : 0.000251793945252067
Modularity : 0.503524
NodeXL Version : 1.0.1.449
Data Import : The graph represents a network of 10,265 Twitter users whose recent tweets contained "#HIV", 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 Friday, 29 July 2022 at 23:16 UTC.
The tweets in the network were tweeted over the 9-day, 1-hour, 6-minute period from Wednesday, 20 July 2022 at 20:23 UTC to Friday, 29 July 2022 at 21:30 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 : #HIV
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:
[1132] #hiv,#aids [1084] keiner,gegen [1017] living,#hiv [897] people,living [824] #hiv,patients [813] human,rights [701] #hiv,response [614] long,acting [612] discrimination,#hiv [610] dedicated,life Top Word Pairs in Tweet in G1:
[524] #hiv,response [489] people,living [444] living,#hiv [387] long,acting [311] #hiv,prevention [234] #hiv,treatment [211] #hiv,#aids [198] living,hiv [197] unaids,global [185] pre,conference Top Word Pairs in Tweet in G2:
[373] #hiv,#aids [137] living,#hiv [135] people,living [79] living,hiv [77] #辅助生殖,#hiv [75] #hiv,prevention [74] #aids,#hiv [74] msdnederland,#hiv [73] long,acting [71] #aids,#hivawareness Top Word Pairs in Tweet in G3:
[605] sad,#humanrights [605] #humanrights,#china [605] #china,lawyer [605] lawyer,#changweiping [605] #changweiping,winner [605] winner,human [605] human,rights [605] rights,prize [605] prize,closed [605] closed,door Top Word Pairs in Tweet in G4:
[1082] keiner,gegen [541] 40jahre,kein [541] kein,#impfstoff [541] #impfstoff,gegen [541] gegen,#hiv [541] #hiv,nach [541] nach,100jahren [541] 100jahren,forschung [541] forschung,keiner [541] gegen,#krebs Top Word Pairs in Tweet in G5:
[65] living,#hiv [59] #pep,#prep [59] #stigma,#diskriminasi [59] #hiv,#uequals2u [59] #uequals2u,#endingaids2030 [57] #prep,#hepc [57] #hepc,#stigma [57] #diskriminasi,#sexed [57] #sexed,#hiv [52] #hiv,virus Top Word Pairs in Tweet in G6:
[246] tigrayan,#rape [246] #rape,survivors [246] survivors,men [246] men,uniform [246] #hiv,positive [242] eucouncil,intlcrimcourt [241] bradsherman,secblinken [241] secblinken,eucouncil [240] uniform,raped [240] told,chosen Top Word Pairs in Tweet in G7:
[63] #hiv,#aids [25] hiv,aids [24] #hepatitis,#hiv [20] #aids,#hiv [18] #liver,#health [17] #hiv,#diabetes [17] #hepatitisc,#hepatitisb [17] world,hepatitis [16] #herpes,#liver [16] #health,#worldhepatitisday Top Word Pairs in Tweet in G8:
[132] living,#hiv [71] people,#hiv [59] #lgbtq,living [59] #hiv,story [59] story,help [59] help,hold [59] hold,government [59] government,agencies [59] agencies,accountable [59] accountable,discrimination Top Word Pairs in Tweet in G9:
[79] #hiv,treatment [78] living,#hiv [48] people,living [43] 54,23 [39] sex,partners [38] hiv,sex [32] end,epidemic [31] #hiv,related [28] treatment,works [28] works,treatment Top Word Pairs in Tweet in G10:
[52] beacon,hope [50] sackville,gardens [48] lost,#hiv [40] living,hiv [36] hiv,stigma [34] rededication,beacon [34] end,hiv [34] stigma,continues [32] hope,sackville [32] important,event Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
Top Replied-To in G5:
Top Replied-To in G6:
Top Replied-To in G7:
Top Replied-To in G8:
Top Replied-To in G9:
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 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:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: