The #medlibs Influencers
Top 10 Influential
![]() | @nikdett 100 |
@ 96 | |
![]() | @tlknott 91 |
![]() | @mehlibrarian 84 |
![]() | @krafty 82 |
![]() | @mascher 81 |
![]() | @aldricham 80 |
![]() | @cdennison 51 |
![]() | @pfanderson 49 |
@ahoyk8 38 |
Prolific Tweeters
![]() | @nikdett 47 |
![]() | @pfanderson 36 |
![]() | @tlknott 24 |
![]() | @mehlibrarian 22 |
![]() | @krafty 16 |
@mscully66 15 | |
![]() | @mascher 15 |
![]() | @7shores 7 |
![]() | @cdennison84 3 |
![]() | @OpenDataIT 1 |
Highest Impressions
![]() | @pfanderson 389.6K |
![]() | @nikdett 67.7K |
![]() | @krafty 36.0K |
![]() | @mehlibrarian 25.4K |
![]() | @tlknott 23.9K |
![]() | @mascher 14.7K |
![]() | @7shores 8.8K |
@mscully66 3.0K | |
![]() | @OpenDataIT 434.0 |
![]() | @cdennison84 300.0 |
The Numbers
19.635KImpressions188Tweets
12Participants
150Avg Tweets/Hour
16Avg Tweets/Participant
Twitter data from the #medlibs hashtag from to () โ Symplur.
Top 10 Influencers is determined by the SymplurRank algorithm.
#medlibs Participants









Data for #medlibs can be up to 15 minutes delayed