Who Are Twitter’s Verified Users?

A deep dive into more than 100,000 of the social media giant’s top users shows that journalists and sports figures dominate the platform.

If you like this article, please follow me (@Haje) on Twitter!

If you spend a fair bit of time on Twitter, you can’t have failed to stumble across users who have a blue tickmark next to their names. These are the Verified Users, and you’re about to learn a lot more about them. Pour a cup of tea, strap in, and let’s go…


There are around 150k verified Twitter accounts in total, and according to Twitter, they are

Highly sought users in music, acting, fashion, government, politics, religion, journalism, media, sports, business and other key interest areas.

A pretty diverse bunch, then.

Twitter currently has around 300m active users, which means that the accounts emblazoned with the coveted Verified badge is a rather exclusive club. Hell, they aren’t even the 1% — they are the 0.05%.

That’s all good and well, of course, but who are they? Well, it turns out some 25 percent of Twitter’s Verified users are actually Journalists. Almost 18 percent, the next largest group, are made up of sports figures and teams.

Let’s dig deeper.

The average Verified Twitter user

The ‘average’ verified Twitter user is a bit of a red herring, because there are some extreme outliers in the list that hugely skew the results.

Katy Perry is the obvious example: with 70m followers, she’s the most-followed Verified Twitter user, and she’ll put a big whacking spanner in any statistical analysis. To show what I mean, have a look at this graph:

Number of followers per Verified Twitter User (n=111,271)

As you can see, the average Verified twitter user has a very high average number of followers (just over 125k, in fact), but the median number of followers is just over 9,000. The Interquartile Mean (i.e. the average of values after the top and bottom quarter of the dataset is lopped off) is around 13,000.

Taking a closer look at the number of friends (i.e. the number of accounts they follow) and the number of Tweets, you some interesting numbers as well:

Number of friends and number of tweets per Verified Twitter User

In other words: We have a group of extremely active twitter users among the verified accounts. @6BillionPeople is living up to their username by apparently trying to follow every Twitter user out there, and @AmexOffers wins the ‘woah, calm down there, cowboy’ prize for having tweeted almost 3m times. Yowzers.

(To keep things moderately simple, I’ll be using the Average values in the rest of the graphs — if you want more details and the interquartile means, you can find those graphics at the bottom of this article.)

So who are they?

The distribution of Verified users isn’t particularly even. That, come to think of it, makes sense: Originally, the Verified programme was designed to help protect and highlight users who were particularly at risk of getting impersonated. As such, it comes as no surprise that sports personalities and news folks are topping the list.

Twitter’s Verified users:

A delicious pie chart of Verified Users, and what they do when they’re not hanging out on social networks all day

Or, if you agree with me that pie charts (despite how deliciously colourful they can be) are better at hiding than showing information, let’s take a look at the same data on a far easier-to-read (if woefully monochromatic) bar chart:

The biggest proportion of Verified users are journalists and assorted media folks (news producers, anchors, TV meteorologists etc) representing almost a quarter of the verified accounts.

They’re followed by sports clubs and athletes with about 18% of the accounts, and actors & entertainers representing another 13%. Given how comprehensively musicians are represented in the top 10 lists, it was surprising to me that only about 12% of the verified accounts were musicians and music industry people.

Comparing the different types of Verified accounts

Of course, as is to be expected, there’s a pretty big difference between how different groups of Twitter users are using Twitter as a tool and as a platform.

For the rest of the graphs here, we’ll take a look at the characteristics of each of the types of Verified twitter follower.

Number of Followers

It’s worth noting that the categories are in the same order as above — i.e. the most common type of verified Twitter account is on the left. I’ve also included the percentages as a reminder, below.

Looking at the above graph, you’ll spot that even though Journalists make up 25% of the Verified users, they have a relatively modest number of followers (avg: 140k). Musicians are the obvious big outlier here, with on average 10x more followers than the average journalist.

Basically, there seems to be a pretty basic — and very obvious — trend: The more famous you are, the more Twitter followers you have. Musicians, Actors, Sports folks and Politicians are in the public eye a lot more than, say, government organisations, and so the followers distribution isn’t hugely surprising.

Number of Friends

The Number of Friends stats (or: The number of people a Verified Twitter account follows themselves) is an interesting distribution as well. A few of the accounts follow vast numbers of other Twitter users.

Number of Tweets

When taking a closer look at who is most active on Twitter, things are suddenly making more sense again — It makes sense that media properties (blogs, big news organisations, etc) and journalists tweet a lot about content they’ve created and breaking news. Businesses tweet quite a lot, too, which makes perfect sense as well: Quite a few of them are running customer support over Twitter, and it makes sense that as the customer base increases, so does the support load via Twitter.

What about follow ratios?

Whereas Facebook and LinkedIn relationships are mutual, Twitter relationships are asymmetrical (you can follow someone without them following you back), which is one of the great things about the platform: It turns Twitter into a bonafide publishing platform.

Taking a look at the ratio between the two numbers — How many people follow you, for each person you follow — gives us some interesting insights into how the different groups use the platform. Or, put differently: If you follow few people but have many followers, you are a broadcaster, much like a news website or a radio station. If the ratio of followers to friends is closer, you’re more likely to use Twitter as a more traditional social network.

Average Ratios

On the above graph, a high bar means many followers and few friends — so it is unsurprising that Media outlets (such as CNN, BBC, etc) and sports stars / sports teams have a high follower to friend ratio.

Interquartile Mean of Ratios

For the ratios, I also felt I needed to take a closer look at the dataset — it turns out that there are a few users who had extreme ratios.

The above graph is looking significantly different than the averages graph above it, and is probably a better representation of how these groups really use Twitter. Journalists are leading the pack: a typical journalist has 4 followers for every person they follow, suggesting that typical journalists use Twitter to communicate and monitor communications. Politicians and Government agencies (with 1:16 and 1:17 ratios, respectively) also have a relatively high degree of interactivity.

At the opposite end of the scale, actors and TV shows have 73 followers for every person they follow, while Music folks and Media outlets have 55 followers for every person they follow. Again, nothing hugely surprising here: Television shows, musicians and media outlets are, by and large, broadcasters, and are using Twitter as a broadcaster.

If you like this article, please follow me (@Haje) on Twitter!

And the Top 10 are?

No post like this would be complete without adding a bunch of Top 10 lists, would it? Of course not; You can find the Top 10 most followed, Top 10 most friended, and Top 10 busiest tweeters in my separate article.

But, just because I can’t really seem to leave well enough alone, and because I’m curious by nature, I give you …

Top 100 by number of followers

1. @katyperry (70.1m)
2. @justinbieber (64.1m)
3. @BarackObama (59.6m)
4. @taylorswift13 (57.9m)
5. @YouTube (51.1m)
6. @ladygaga (46.8m)
7. @rihanna (45.8m)
8. @jtimberlake (45.6m)
9. @TheEllenShow (43.6m)
10. @britneyspears (41.9m)
11. @twitter (39.6m)
12. @instagram (39m)
13. @Cristiano (35.7m)
14. @JLo (32.1m)
15. @KimKardashian (32m)
16. @shakira (31.3m)
17. @selenagomez (29.2m)
18. @ArianaGrande (28.9m)
19. @ddlovato (28.5m)
20. @Oprah (27.6m)
21. @cnnbrk (26.8m)
22. @jimmyfallon (25.4m)
23. @Harry_Styles (24.7m)
24. @onedirection (23.6m)
25. @Drake (23m)
26. @LilTunechi (22.9m)
27. @KAKA (22.7m)
28. @OfficialAdele (22.7m)
29. @NiallOfficial (22.3m)
30. @BillGates (22.2m)
31. @aliciakeys (21.8m)
32. @KingJames (21.3m)
33. @BrunoMars (21m)
34. @pitbull (20.2m)
35. @Real_Liam_Payne (20.2m)
36. @wizkhalifa (19.7m)
37. @Louis_Tomlinson (19.6m)
38. @MileyCyrus (19.5m)
39. @Eminem (19.4m)
40. @NICKIMINAJ (19.3m)
41. @KevinHart4real (19.2m)
42. @espn (19.1m)
43. @AvrilLavigne (18.8m)
44. @neymarjr (18.3m)
45. @EmWatson (17.9m)
46. @davidguetta (17.6m)
47. @SportsCenter (17.3m)
48. @CNN (17.2m)
49. @danieltosh (16.9m)
50. @aplusk (16.9m)
51. @nytimes (16.8m)
52. @ActuallyNPH (16.1m)
53. @realmadrid (16m)
54. @MariahCarey (15.5m)
55. @BBCBreaking (15.4m)
56. @FCBarcelona (15.1m)
57. @SrBachchan (14.9m)
58. @xtina (14.8m)
59. @coldplay (14.7m)
60. @NBA (14.6m)
61. @zaynmalik (14.6m)
62. @JimCarrey (14.4m)
63. @kourtneykardash (14.4m)
64. @chrisbrown (14.2m)
65. @vine (14.1m)
66. @facebook (13.9m)
67. @edsheeran (13.8m)
68. @RyanSeacrest (13.5m)
69. @ParisHilton (13.3m)
70. @iamsrk (13.2m)
71. @iamwill (13.2m)
72. @agnezmo (13.2m)
73. @ivetesangalo (12.9m)
74. @LeoDiCaprio (12.8m)
75. @ashleytisdale (12.8m)
76. @aamir_khan (12.7m)
77. @tyrabanks (12.6m)
78. @narendramodi (12.5m)
79. @kanyewest (12.4m)
80. @AlejandroSanz (12.4m)
81. @MTV (12.3m)
82. @blakeshelton (12.2m)
83. @SnoopDogg (12.2m)
84. @ricky_martin (12.2m)
85. @BeingSalmanKhan (12m)
86. @SimonCowell (12m)
87. @nfl (11.5m)
88. @charliesheen (11.5m)
89. @WayneRooney (11.3m)
90. @google (11.2m)
91. @ClaudiaLeitte (11.1m)
92. @DalaiLama (11m)
93. @maroon5 (10.9m)
94. @UberFacts (10.8m)
95. @carlyraejepsen (10.8m)
96. @ZacEfron (10.7m)
97. @KendallJenner (10.7m)
98. @SamsungMobile (10.6m)
99. @LucianoHuck (10.6m)
100. @marcosmion (10.6m)

Top 100 by number of friends

(i.e. the number of people they follow)

1. @6BillionPeople (2.4m)
2. @hootsuite (1.6m)
4. @yokoono (970k)
5. @SleepSkee (860k)
6. @colortheory (840k)
7. @benlandis (810k)
8. @Radioblogger (810k)
9. @miguelhotero (800k)
10. @lonelyplanet (700k)
11. @threadless (670k)
12. @BarackObama (640k)
13. @FreddyAmazin (600k)
14. @sherylunderwood (570k)
15. @fuzethemc (570k)
16. @DjKingAssassin (550k)
17. @JohnCMaxwell (540k)
18. @WholeFoods (540k)
19. @Cooperativa (520k)
20. @Spruke (520k)
21. @Emol (510k)
22. @StartupPro (490k)
23. @JoeyBats19 (470k)
24. @RayWJ (470k)
25. @alispagnola (440k)
26. @shwood (440k)
27. @MrKRudd (410k)
28. @britneyspears (400k)
29. @LFC (400k)
30. @TayeDiggs (390k)
31. @PleasureEllis (390k)
32. @thebeatles (390k)
33. @ElNacionalWeb (370k)
34. @NOH8Campaign (350k)
35. @BreadBoi (340k)
36. @BenjaminEnfield (330k)
37. @gimmemotalk (320k)
38. @thejimjams (320k)
39. @PenguinUKBooks (320k)
40. @ClaroArgentina (320k)
41. @JohnEMichel (320k)
42. @mallikasherawat (300k)
43. @ConsiderMeDead (300k)
44. @Variety (300k)
45. @ammr (280k)
46. @chessqueen (280k)
47. @AdelineMx (280k)
48. @_ChadKowal (280k)
49. @spacemarch (280k)
50. @iamtherealtaj (280k)
51. @Sephora (280k)
52. @toddcarey (280k)
53. @charitywater (280k)
54. @stonefiregrill (280k)
55. @MoeRockOnline (270k)
56. @viajaVolaris (270k)
57. @KameronBennett (270k)
58. @NateMaingard (270k)
59. @Interior (270k)
60. @maddijanemusic (260k)
61. @KINGLilKeis (260k)
62. @BillZucker (260k)
63. @samsung (250k)
64. @sandikrakowski (250k)
65. @Interscope (240k)
66. @latercera (240k)
67. @thinkgeek (240k)
68. @SFMOMA (240k)
69. @kinseyschofield (230k)
70. @DanielGoddard (230k)
71. @livestrong (230k)
72. @Korn (230k)
73. @SEGA (230k)
74. @EminikOfficial (230k)
75. @gavinmikhail (220k)
76. @sport (220k)
77. @TOMS (220k)
78. @McInTweet (220k)
79. @zerohora (220k)
80. @yvesjean (220k)
81. @Diego_Arria (220k)
82. @nucfootball (220k)
83. @mariashriver (210k)
84. @justinbieber (210k)
85. @designtaxi (210k)
86. @Rene (210k)
87. @CodySimpson (210k)
88. @yo (210k)
89. @corbinbleu (210k)
90. @HRC (200k)
91. @WeAreTheInCrowd (200k)
92. @IslandRecords (200k)
93. @JohnBoy (200k)
94. @Lennar (200k)
95. @Calle7_TVN (200k)
96. @DominicScott (200k)
97. @folha (190k)
98. @theoduscrane (190k)
99. @DefJamRecords (190k)
100. @JuliaGillard (190k)

Top 100 by number of tweets

1. @AmexOffers (2.9m)
2. @CocaNoMc (2m)
3. @MovistarArg (1.8m)
4. @la_patilla (1.8m)
5. @Telkomsel (1.5m)
6. @PersonalAr (1.5m)
7. @ElNacionalWeb (1.2m)
8. @virginmedia (1.1m)
9. @VZWSupport (1.1m)
10. @noticias24 (950k)
11. @AmericanAir (900k)
12. @Tesco (900k)
13. @BTCare (820k)
14. @detikcom (810k)
15. @ATTCares (810k)
16. @ElUniversal (790k)
17. @BCCI (780k)
18. @indosatcare (750k)
19. @O2 (720k)
20. @ClaroArgentina (690k)
21. @ATVIAssist (670k)
22. @SkyHelpTeam (670k)
23. @sprintcare (640k)
24. @NigeriaNewsdesk (610k)
25. @Shorouk_News (610k)
26. @PublimetroChile (590k)
27. @KLM (570k)
28. @ASOS_HeretoHelp (530k)
29. @teleSURtv (530k)
30. @FamousBirthdays (510k)
31. @VIVAcoid (510k)
32. @StradeANAS (510k)
33. @SinEmbargoMX (500k)
34. @Cooperativa (500k)
35. @NS_online (500k)
36. @Metro_TV (490k)
37. @SafaricomLtd (490k)
38. @XLCare (460k)
39. @Battlefield (450k)
40. @BofA_Help (450k)
41. @IAmRichTheKid (450k)
42. @TWC_Help (450k)
43. @DeltaAssist (450k)
44. @Cicmty (440k)
45. @united (440k)
46. @rcnlaradio (430k)
47. @VTVcanal8 (430k)
48. @HuffingtonPost (410k)
49. @vodafoneNL (410k)
50. @ThreeUKSupport (410k)
51. @NikeSupport (400k)
52. @pontofrio (400k)
53. @greateranglia (390k)
54. @TelkomCare (390k)
55. @VirginTrains (380k)
56. @MMDA (380k)
57. @FGW (380k)
58. @DIRECTVServicio (380k)
59. @ChipotleTweets (370k)
60. @EE (370k)
61. @RoyalMail (370k)
62. @fmfukuoka (370k)
63. @VodafoneIN (370k)
64. @g1 (360k)
65. @MeridianoTV (360k)
66. @telegraaf (350k)
67. @victoria1039fm (350k)
68. @Antena2RCN (350k)
69. @talk2GLOBE (350k)
70. @Netflixhelps (350k)
71. @Walmart (340k)
72. @LondonMidland (340k)
73. @GlennF (340k)
74. @Safaricom_Care (340k)
75. @inquirerdotnet (340k)
76. @Telstra (330k)
77. @diariopanorama (330k)
78. @gmanews (320k)
79. @temponewsroom (320k)
80. @Tahrir_News (320k)
81. @pizzahut (320k)
82. @NoticiasCaracol (310k)
83. @elespectador (310k)
84. @ElliottWilson (310k)
85. @latercera (310k)
86. @monaeltahawy (310k)
87. @VodafoneEgypt (310k)
88. @VodafoneUKhelp (310k)
89. @ZiggoWebcare (310k)
90. @VerizonSupport (300k)
91. @SpotifyCares (300k)
92. @24HorasTVN (300k)
93. @KingGage_ (290k)
94. @Milenio (290k)
95. @El_Universal_Mx (290k)
96. @VodafoneUK (290k)
97. @manila_bulletin (290k)
98. @JhTV3 (280k)
99. @bomani_jones (280k)
100. @Hootsuite_Help (280k)

Okay, so this is the boring bit, but you may be interested if you’re a little bit of a nerd. Feel free to stop reading and go share the Top 10 lists with your Twitter followers …


I spent quite a bit of time (and some money, too) in pulling all of this information together; Given that people may wish to refer back to it later, they might also want to know where the data came from and how it was treated, so here’s some techy, geeky stuff about the above.

All data was gathered between 20 and 25 May 2015.

Data gathering & Filtering

This piece of research assumes that the majority of Verified accounts are followed by @Verified, and that assumption is the basis for this data set. Step 1 was to use the Twitter API and some custom-written tools to grab the biographies for all 130k or so accounts followed by @Verified, and storing it temporarily for analysis.

Once I had a full database of all these users, I filtered out all non-Latin-language accounts, mostly for practical reasons. In my small test batch, these accounts did very poorly in the categorisation step (more about that in just a minute), and the amount of time it would have taken to use Google Translate etc to categorise them means it wouldn’t be cost effective to categorise them. Overall, this meant that my data set shrunk by approximately 15%, but I feel this is still a representative sample of Twitter’s Verified users.

Overall, I spent a few days gathering all the data I needed for this, largely because I kept butting into Twitter’s API limits. A smarter coder than myself might have been able to do it faster, but smart coders, frankly, know better than to look at my code. It ain’t pretty, but it works.

At the end of the gathering and filtering phase, I had around 112k accounts left in my data set.


In addition to the full data set, I wanted to learn more about the people who operate Twitter Verified accounts, because my hypothesis was that there’s a pretty big difference between the various Verified users.

To help me do that, I wrote a tool that uses Amazon’s Mechanical Turk to categorise a random selection these accounts. The accounts were selected at random by pulling them out of the MySQL database using ORDER BY RAND(). Once I had a selection of accounts for further analysis, the Mechanical Turk workers grouped the accounts into categories:

  1. Actors / Entertainers
  2. Companies (excluding ones that fit in other categories)
  3. Government institutions and NGOs)
  4. Journalists, TV Presenters, and news people
  5. Media outlets (BBC, CNN, Wired Magazine, etc)
  6. Musicians, bands, and music people (including producers, record labels etc)
  7. Politicians (i.e. people who primarily are politicians)
  8. Athletes, sports teams, sports organising bodies (including NBA, FIFA, etc)
  9. Shows or movies (TV shows, podcasts, radio shows, movies, shorts, and accounts specifically tied to TV commercials etc etc)
  10. Other / Unknown.

I manually went through and did another pass of Other / Unknown to try to categorise these. I was successful in about 50% of the cases, but some of the accounts did leave me stumped. I’ve left them out of the dataset, which shaved off an additional 1.5% of the data.

In total, just over 15,000 accounts were categorised into the above categories, and are the representative sample for further analysis into the differences between the various groups of Twitter users.

From there, I ran a series of queries on the database to find out what a typical Twitter user looked like in each of the above categories, and then used the ever-so-glamorous Google Docs to create a series of graphs to visualize the results.

If you like this article, please follow me (@Haje) on Twitter!

Advanced graphs

In this article, I’m mostly using Averages, as that’s what people are instinctively more likely to understand. Below, I’m reproducing the Average and IQM graphs.

Number of followers

Number of Friends

Number of Tweets