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How Stuff Spreads
Francesco D’Orazio, @abc3d
#SMWF NYC

pulsarplatform.com


Based on a study by Francesco D’Orazio (@abc3d) and Jess Owens (@hautepop)
Why do we share?!
Emotion is the
trigger
Relevance to our community
provides validation 
(topicality)
Relevance to our community
provides validation 
(timeliness)
How Stuff Spreads: how video goes viral
Gatekeepers activate the
communities within the audience
and escalate the diffusion
So given the right content, audience relevance and influencer
push, virality should always happen in the same way.

Except it never does	
  
We looked at 4 memes that have “gone viral”:
a music video, an ad, a citizen journalism video, a web series	
  
0
10,000
20,000
30,000
40,000
50,000
60,000
11-May 18-May 25-May 01-Jun
Launched	
  at	
  10pm	
  
GMT	
  on	
  12	
  May,	
  &	
  
gets	
  11,400	
  Twi<er	
  
shares	
  in	
  2	
  hours	
  	
  
Peaks	
  at	
  51,600	
  
shares	
  on	
  13	
  May	
  
Within	
  a	
  week	
  it's	
  
below	
  1000	
  shares	
  
per	
  day	
  	
  (17	
  May)	
  
Perfect	
  power	
  law	
  
decay	
  –	
  no	
  spikes	
  aLer	
  
launch	
  aLer	
  a	
  big	
  
influencer	
  finds	
  it	
  
belatedly	
  
How Stuff Spreads: how video goes viral
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
15-Apr 22-Apr 29-Apr 06-May 13-May 20-May 27-May 03-Jun 10-Jun
ConPnuing	
  ripples	
  even	
  a	
  
month	
  aLer	
  a	
  launch,	
  as	
  	
  new	
  
communiPes	
  and	
  community	
  
influencers	
  discover	
  the	
  video	
  
600	
  people	
  find	
  	
  &	
  tweet/RT	
  
the	
  video	
  on	
  15	
  April,	
  before	
  
Dove	
  officially	
  tweet	
  it	
  
(@Dove_Canada	
  on	
  16th)	
  
Peaks	
  on	
  Day	
  3,	
  the	
  17	
  April.	
  
Doesn't	
  show	
  the	
  rapid	
  
power-­‐law	
  decay	
  of	
  the	
  news-­‐
driven	
  searches	
  
Secondary	
  peaks	
  when	
  it	
  
spreads	
  into	
  new	
  
communiPes	
  &	
  is	
  noPced	
  by	
  
new	
  influencers.	
  E.g.	
  
@DoveUKI	
  on	
  19	
  Apr	
  
How Stuff Spreads: how video goes viral
0
2,000
4,000
6,000
8,000
10,000
12,000
01-Jun 08-Jun 15-Jun 22-Jun
Very	
  sharp	
  decay	
  for	
  this	
  
news-­‐driven	
  video,	
  which	
  
gained	
  its	
  value	
  from	
  showing	
  
events	
  in	
  Gezi	
  Park	
  when	
  
Turkish	
  TV	
  channels	
  weren't.	
  
Day	
  3:	
  only	
  197	
  shares	
  
How Stuff Spreads: how video goes viral
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
21-Apr 28-Apr 05-May 12-May 19-May 26-May 02-Jun 09-Jun 16-Jun 23-Jun
Unlike	
  other	
  videos	
  this	
  is	
  
serialised	
  content.	
  Peaks	
  when	
  
(a)	
  new	
  video	
  released	
  
(b)	
  picked	
  up	
  by	
  top	
  influenPal	
  
Vine	
  account	
  
How Stuff Spreads: how video goes viral
@abc3d | PulsarPlatform.com
Virality Quantified!
Which variables are best for identifying a viral
phenomenon?
15.9m
59m
1.01m
L No view count on
Views	
  
81,200!Tweets!
64,900!Tweets!
12,940!
Tweets!
30,280!
Tweets!
75,067!
Unique Authors!
62,324!
Unique Authors!
11,868!
Unique Authors!
27,993!
Unique Authors!
How Stuff Spreads: how video goes viral
197%!
194%!
355%!
435%!
Dove Real Beauty!
Ryan Gosling!
Cmdr Hadfield!
Turkish protest!
Coefficient of attention variation (%)!
Volatility varies!
0
10000
20000
30000
40000
50000
60000
1 8 15 22 29 36 43 50 57
Commander Hadfield
Dove
Turkey
Ryan Gosling
Days	
  since	
  video	
  launch	
  
1Day!
1Day!
3 Days! 18 Days!
Time to Peak varies (shares/day)!
!
0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Commander Hadfield
Dove
Turkey
Ryan Gosling
Velocity varies (shares/hour on peak day)!
1,088!
5,108!
12,886!
Dove Real Beauty!
Ryan Gosling!
Cmdr Hadfield!
Turkish protest!
Social currency (shares per 1m views)!
Shareability varies!
L No view count on
20
8
8
2
Dove Real Beauty!
Ryan Gosling!
Cmdr Hadfield!
Turkish protest!
Lifespan varies (continuous period at 500 shares/day)!
Although none of the variables alone
proved useful to identify a viral
phenomenon, all of them correlate around
two main models of viral spread 	
  
Spikers vs Growers!

High Volatility"
Fast to Peak
High Velocity
High Shareability
Shorter Lifespan
	
  

Lower Volatility"
Slower to Peak
Lower Velocity
Lower Shareability
Longer Lifespan
	
  
But what makes a meme spread along the
first or the second model? 	
  
How Stuff Spreads: how video goes viral
All the videos stimulated a similar higher than average
emotional reaction."

(52-56/100 Sensum Score / Based on GSR).	
  
	
  
So can the audience composition instead explain why
memes develop along one of the other model?
	
  
35
30
34
29
Dove Real Beauty!
Ryan Gosling!
Cmdr Hadfield!
Turkish protest!
All memes were similarly amplified

(average Visibility of a post containing the meme)!
75%!
63%!
14%!
34%!
Globality rate varied!
(% of shares from countries other than the top one)!
Since both Amplification and Globality
seemed not to correlate with one or the
other model of virality we then looked at the
demographics engaged with each meme 	
  
30 Years!
66%	
  
34%	
  
White!
Christian 55%!
Jewish36%!
!
Students 9%!
Journalists9%!
Web devs 8%!
Senior Managers 7%!
Musicians 6%!
!
@NASA!
@StephenFry!
@BarackObama!
@DalaiLama!
@Conan O’Brien!
!
Technology!
Science News!
Photography!
Music!
Comedy!
!
London 11%!
Toronto5%!
New York 3%!
Dublin 3%!
Vancouver 2%!
!
19 Years!
21%	
  
79%	
  
White 81% !
Black!
Hispanic!
!
Christian 67%!
Muslim 24%!
!
Students 15%!
Sales 10%!
Journalists 4%!
Photographers!
Artists!
Stylists!
Admin Staff!
@KatyPerry!
@E.DeGeneres!
@TaylorSwift!
@JustinBieber!
@LadyGaga!
@KimKardashian!
!
Comedy!
Music!
Fashion!
TV/Film!
Health Issues!
Sports!
!
London 5%!
Toronto 5%!
New York 4%!
Riyadh 3%!
26 Years!
50%	
  50%	
  
White 99% !
Muslim 94%!
!
Students 12%!
Musicians 8%!
Senior Managers 8%!
Web Developers!
Journalists!
Engineers!
Graphic Designer!
Teachers!
@CemYilmaz!
@SertabErener!
@AbdullahGül!
@BarackObama!
@ConanO’Brien!
@WikiLeaks!
@Nytimes!
@BBCNews!
!
Politics!
News!
Tech!
Football!
Music!
!
Instanbul 50%!
Izmir 32%!
Ankara 4%!
Bursa 1%!
18 Years!
26%	
  
74%	
  
White !
Black!
Hispanic!
!
Christian 84%!
Muslim 9%!
!
Students 33%!
Musicians 13%!
Actors 4%!
@JustinBieber!
@TaylorSwift!
@KatyPerry!
@MileyCyrus!
@DanielTosh!
@SnookiPolizzi!
!
Comedy!
Music!
Dating!
Extreme Sports!
!
NYC 6%!
London 3%!
Los Angeles 2%!
Chicago 2%!
As we couldn’t find any correlation between demographic
traits and virality models we then turned to the structure of
the audience by mapping the social graph (followers/
friends) of the people who shared the meme	
  
11.22
6.84
Audience connectedness (avg degree)!
4.26
3.14
Highly connected audiences (higher
average degree in the audience network)
make the meme spread faster	
  
0.506
0.466
Audience fragmentation (modularity)!
0.752
0.650
High audience fragmentation into sub-
communities (high modularity of the audience
network) makes the meme spread slower	
  
130
communities!
!
3 !
connect up to 50% of
the audience!
1356!
communities!
!
8 !
connect up to 50% of
the audience!
51!
communities!
!
2!
connect up to 50% of
the audience!
!
382!
communities!
!
5 !
connect up to 50% of
the audience!
!
130!
communities!
51!
communities!
!
1356!
communities!
387!
communities!
But what is causing higher or lower
fragmentation within an audience? 	
  
32, male, white, CAN/USA,
into science, tech and
comedy
30, male, white, UK, into
tech, comedy and music
32, female, white, USA/NYC,
marketing professional
16, female, white/hispanic, USA/
LA, into teen pop and reality tv
25, mixed, white, Turkey/Istanbul,
into politics, sports, web
21, mixed, white, Turkey/Izmir,
into politics, sports, web
17, female, white/black/
hispanic, USA/Texas, into
teen pop and reality tv
19, female, white, Global,
into comedy, music, tv
High demographic diversity correlates with
high modularity and slower meme velocity	
  
So, what’s the point?!
How Stuff Spreads: how video goes viral
There is no such thing as “virality”	
  
How Stuff Spreads: how video goes viral
“Virality” is a relative concept depending on
the audience of reference	
  
“Virality” is not just a property of the content, it’s also a
property of the audience. 

Or as Jonah Peretti put it, Virality is 50% great content
and 50% distribution	
  
Great content spreads fast or slow depending on the shape of
your audience and how you are leveraging it with your
distribution strategy	
  
The audience you are trying to reach is fragmented into 
sub-communities of age, profession, interest	
  
Using network analysis you can identify these communities by
mapping the social graph of your target audience	
  
The broader the appeal of your content the more fragmented
your audience is going to be	
  
The more fragmented the audience, 
the more targeted the distribution needs to be	
  
Wide appeal = Grower = spend more on seeding strategy to
connect communities and sustain diffusion over time

Narrow appeal = Spiker = spend more on community
management to absorb + amplify impact	
  
So if you want your content to go viral, don’t just put the video
out there and see what happens…
Study your target audience and plan your distribution strategy
based on a community-map,	
  not just on a list of
“influencers” (who might all be part of the same community)	
  
Thank You!
Francesco D’Orazio, @abc3d
#SMWF NYC

pulsarplatform.com


Based on a study by Francesco D’Orazio (@abc3d) and Jess Owens (@hautepop)

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How Stuff Spreads: how video goes viral

  • 1. How Stuff Spreads Francesco D’Orazio, @abc3d #SMWF NYC pulsarplatform.com Based on a study by Francesco D’Orazio (@abc3d) and Jess Owens (@hautepop)
  • 2. Why do we share?!
  • 4. Relevance to our community provides validation (topicality)
  • 5. Relevance to our community provides validation (timeliness)
  • 7. Gatekeepers activate the communities within the audience and escalate the diffusion
  • 8. So given the right content, audience relevance and influencer push, virality should always happen in the same way. Except it never does  
  • 9. We looked at 4 memes that have “gone viral”: a music video, an ad, a citizen journalism video, a web series  
  • 10. 0 10,000 20,000 30,000 40,000 50,000 60,000 11-May 18-May 25-May 01-Jun Launched  at  10pm   GMT  on  12  May,  &   gets  11,400  Twi<er   shares  in  2  hours     Peaks  at  51,600   shares  on  13  May   Within  a  week  it's   below  1000  shares   per  day    (17  May)   Perfect  power  law   decay  –  no  spikes  aLer   launch  aLer  a  big   influencer  finds  it   belatedly  
  • 12. 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 15-Apr 22-Apr 29-Apr 06-May 13-May 20-May 27-May 03-Jun 10-Jun ConPnuing  ripples  even  a   month  aLer  a  launch,  as    new   communiPes  and  community   influencers  discover  the  video   600  people  find    &  tweet/RT   the  video  on  15  April,  before   Dove  officially  tweet  it   (@Dove_Canada  on  16th)   Peaks  on  Day  3,  the  17  April.   Doesn't  show  the  rapid   power-­‐law  decay  of  the  news-­‐ driven  searches   Secondary  peaks  when  it   spreads  into  new   communiPes  &  is  noPced  by   new  influencers.  E.g.   @DoveUKI  on  19  Apr  
  • 14. 0 2,000 4,000 6,000 8,000 10,000 12,000 01-Jun 08-Jun 15-Jun 22-Jun Very  sharp  decay  for  this   news-­‐driven  video,  which   gained  its  value  from  showing   events  in  Gezi  Park  when   Turkish  TV  channels  weren't.   Day  3:  only  197  shares  
  • 16. 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 21-Apr 28-Apr 05-May 12-May 19-May 26-May 02-Jun 09-Jun 16-Jun 23-Jun Unlike  other  videos  this  is   serialised  content.  Peaks  when   (a)  new  video  released   (b)  picked  up  by  top  influenPal   Vine  account  
  • 18. @abc3d | PulsarPlatform.com Virality Quantified! Which variables are best for identifying a viral phenomenon?
  • 19. 15.9m 59m 1.01m L No view count on Views  
  • 22. 197%! 194%! 355%! 435%! Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! Coefficient of attention variation (%)! Volatility varies!
  • 23. 0 10000 20000 30000 40000 50000 60000 1 8 15 22 29 36 43 50 57 Commander Hadfield Dove Turkey Ryan Gosling Days  since  video  launch   1Day! 1Day! 3 Days! 18 Days! Time to Peak varies (shares/day)! !
  • 24. 0 1000 2000 3000 4000 5000 6000 7000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Commander Hadfield Dove Turkey Ryan Gosling Velocity varies (shares/hour on peak day)!
  • 25. 1,088! 5,108! 12,886! Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! Social currency (shares per 1m views)! Shareability varies! L No view count on
  • 26. 20 8 8 2 Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! Lifespan varies (continuous period at 500 shares/day)!
  • 27. Although none of the variables alone proved useful to identify a viral phenomenon, all of them correlate around two main models of viral spread  
  • 28. Spikers vs Growers! High Volatility" Fast to Peak High Velocity High Shareability Shorter Lifespan   Lower Volatility" Slower to Peak Lower Velocity Lower Shareability Longer Lifespan  
  • 29. But what makes a meme spread along the first or the second model?  
  • 31. All the videos stimulated a similar higher than average emotional reaction." (52-56/100 Sensum Score / Based on GSR).    
  • 32. So can the audience composition instead explain why memes develop along one of the other model?  
  • 33. 35 30 34 29 Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! All memes were similarly amplified
 (average Visibility of a post containing the meme)!
  • 34. 75%! 63%! 14%! 34%! Globality rate varied! (% of shares from countries other than the top one)!
  • 35. Since both Amplification and Globality seemed not to correlate with one or the other model of virality we then looked at the demographics engaged with each meme  
  • 36. 30 Years! 66%   34%   White! Christian 55%! Jewish36%! ! Students 9%! Journalists9%! Web devs 8%! Senior Managers 7%! Musicians 6%! ! @NASA! @StephenFry! @BarackObama! @DalaiLama! @Conan O’Brien! ! Technology! Science News! Photography! Music! Comedy! ! London 11%! Toronto5%! New York 3%! Dublin 3%! Vancouver 2%! !
  • 37. 19 Years! 21%   79%   White 81% ! Black! Hispanic! ! Christian 67%! Muslim 24%! ! Students 15%! Sales 10%! Journalists 4%! Photographers! Artists! Stylists! Admin Staff! @KatyPerry! @E.DeGeneres! @TaylorSwift! @JustinBieber! @LadyGaga! @KimKardashian! ! Comedy! Music! Fashion! TV/Film! Health Issues! Sports! ! London 5%! Toronto 5%! New York 4%! Riyadh 3%!
  • 38. 26 Years! 50%  50%   White 99% ! Muslim 94%! ! Students 12%! Musicians 8%! Senior Managers 8%! Web Developers! Journalists! Engineers! Graphic Designer! Teachers! @CemYilmaz! @SertabErener! @AbdullahGül! @BarackObama! @ConanO’Brien! @WikiLeaks! @Nytimes! @BBCNews! ! Politics! News! Tech! Football! Music! ! Instanbul 50%! Izmir 32%! Ankara 4%! Bursa 1%!
  • 39. 18 Years! 26%   74%   White ! Black! Hispanic! ! Christian 84%! Muslim 9%! ! Students 33%! Musicians 13%! Actors 4%! @JustinBieber! @TaylorSwift! @KatyPerry! @MileyCyrus! @DanielTosh! @SnookiPolizzi! ! Comedy! Music! Dating! Extreme Sports! ! NYC 6%! London 3%! Los Angeles 2%! Chicago 2%!
  • 40. As we couldn’t find any correlation between demographic traits and virality models we then turned to the structure of the audience by mapping the social graph (followers/ friends) of the people who shared the meme  
  • 42. Highly connected audiences (higher average degree in the audience network) make the meme spread faster  
  • 44. High audience fragmentation into sub- communities (high modularity of the audience network) makes the meme spread slower  
  • 45. 130 communities! ! 3 ! connect up to 50% of the audience!
  • 46. 1356! communities! ! 8 ! connect up to 50% of the audience!
  • 47. 51! communities! ! 2! connect up to 50% of the audience! !
  • 48. 382! communities! ! 5 ! connect up to 50% of the audience! !
  • 50. But what is causing higher or lower fragmentation within an audience?  
  • 51. 32, male, white, CAN/USA, into science, tech and comedy 30, male, white, UK, into tech, comedy and music 32, female, white, USA/NYC, marketing professional 16, female, white/hispanic, USA/ LA, into teen pop and reality tv 25, mixed, white, Turkey/Istanbul, into politics, sports, web 21, mixed, white, Turkey/Izmir, into politics, sports, web 17, female, white/black/ hispanic, USA/Texas, into teen pop and reality tv 19, female, white, Global, into comedy, music, tv
  • 52. High demographic diversity correlates with high modularity and slower meme velocity  
  • 53. So, what’s the point?!
  • 55. There is no such thing as “virality”  
  • 57. “Virality” is a relative concept depending on the audience of reference  
  • 58. “Virality” is not just a property of the content, it’s also a property of the audience. Or as Jonah Peretti put it, Virality is 50% great content and 50% distribution  
  • 59. Great content spreads fast or slow depending on the shape of your audience and how you are leveraging it with your distribution strategy  
  • 60. The audience you are trying to reach is fragmented into sub-communities of age, profession, interest  
  • 61. Using network analysis you can identify these communities by mapping the social graph of your target audience  
  • 62. The broader the appeal of your content the more fragmented your audience is going to be  
  • 63. The more fragmented the audience, the more targeted the distribution needs to be  
  • 64. Wide appeal = Grower = spend more on seeding strategy to connect communities and sustain diffusion over time Narrow appeal = Spiker = spend more on community management to absorb + amplify impact  
  • 65. So if you want your content to go viral, don’t just put the video out there and see what happens…
  • 66. Study your target audience and plan your distribution strategy based on a community-map,  not just on a list of “influencers” (who might all be part of the same community)  
  • 67. Thank You! Francesco D’Orazio, @abc3d #SMWF NYC pulsarplatform.com Based on a study by Francesco D’Orazio (@abc3d) and Jess Owens (@hautepop)