The graph represents a network of 17,970 Twitter users whose recent tweets contained "opioid", 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 Thursday, 26 March 2020 at 17:31 UTC.
The tweets in the network were tweeted over the 3-day, 17-hour, 40-minute period from Sunday, 22 March 2020 at 23:22 UTC to Thursday, 26 March 2020 at 17:02 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 : 17970
Unique Edges : 21399
Edges With Duplicates : 3117
Total Edges : 24516
Number of Edge Types : 5
Mentions : 3841
Retweet : 7943
Tweet : 2203
MentionsInRetweet : 8437
Replies to : 2092
Self-Loops : 2284
Reciprocated Vertex Pair Ratio : 0.00957798521975885
Reciprocated Edge Ratio : 0.0189742354924151
Connected Components : 2094
Single-Vertex Connected Components : 1126
Maximum Vertices in a Connected Component : 13172
Maximum Edges in a Connected Component : 19415
Maximum Geodesic Distance (Diameter) : 21
Average Geodesic Distance : 5.1626
Graph Density : 6.43072517180318E-05
Modularity : 0.764643
NodeXL Version : 1.0.1.428
Data Import : The graph represents a network of 17,970 Twitter users whose recent tweets contained "opioid", 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 Thursday, 26 March 2020 at 17:31 UTC.
The tweets in the network were tweeted over the 3-day, 17-hour, 40-minute period from Sunday, 22 March 2020 at 23:22 UTC to Thursday, 26 March 2020 at 17:02 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 : opioid
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:
[4997] republican,attorney [4789] attorney,texas [4307] over,opioid [4158] trump,appointees [4141] opioid,dispensing [3893] walmart,over [3850] indict,walmart [3754] wanted,indict [3729] texas,wanted [3728] appointees,doj Top Word Pairs in Tweet in G1:
[3676] over,opioid [3567] opioid,dispensing [3319] walmart,over [3258] trump,appointees [3248] republican,attorney [3247] attorney,texas [3212] indict,walmart [3211] wanted,indict [3209] texas,wanted [3208] dispensing,trump Top Word Pairs in Tweet in G2:
[1374] attorney,texas [1370] republican,attorney [992] political,appointees [978] opioid,epidemic [972] pharmacists,protested [972] kept,filling [972] filling,suspicious [972] suspicious,prescriptions [972] prescriptions,stoking [972] stoking,country Top Word Pairs in Tweet in G3:
[1281] suicide,opioid [1279] number,deaths [1279] deaths,severe [1279] severe,economic [1279] economic,meltdown [1279] meltdown,suicide [1279] opioid,alcohol [1279] alcohol,drug [1279] drug,abuse [1279] abuse,depression Top Word Pairs in Tweet in G4:
[1208] numbers,coronavirus [1208] coronavirus,compares [1208] compares,flu [1208] flu,opioid [1208] opioid,overdoses [35] number,deaths [35] deaths,severe [35] severe,economic [35] economic,meltdown [35] meltdown,suicide Top Word Pairs in Tweet in G5:
[149] killed,indictment [148] trump,appointees [146] appointees,killed [143] opioid,crisis [133] avoided,opioid [133] opioid,charges [131] walmart,avoided [130] charges,trump [112] opioid,epidemic [102] opioid,addiction Top Word Pairs in Tweet in G6:
[222] #opioidcrisis,#opioid [151] anti,opioid [114] intractable,pain [105] dear,anti [105] opioid,folks [105] folks,isolation [105] isolation,being [105] being,go [105] go,work [105] work,movies Top Word Pairs in Tweet in G7:
[587] pelosi,bill [587] bill,includes [587] includes,provision [587] provision,corporate [587] corporate,board [587] board,diversity [587] diversity,evidence [587] evidence,such [587] such,measures [587] measures,produce Top Word Pairs in Tweet in G8:
[60] opioid,crisis [36] opioid,epidemic [20] opioid,overdoses [18] opioid,addiction [11] barnes_law,using [11] using,freezer [11] freezer,trucks [11] trucks,opioid [11] overdoses,small [11] small,cities Top Word Pairs in Tweet in G9:
[58] opioid,crisis [40] opioid,epidemic [35] opioid,addiction [25] coronavirus,wreaked [25] wreaked,havoc [25] havoc,state [25] state,legislatures [25] legislatures,derailing [25] derailing,policy [25] policy,agendas Top Word Pairs in Tweet in G10:
[231] republican,attorney [217] opioid,epidemic [212] suspicious,prescriptions [211] pharmacists,protested [211] kept,filling [211] filling,suspicious [211] prescriptions,stoking [211] stoking,country [211] country,opioid [211] epidemic,republican 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 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 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:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: