Why do some videos go viral while others collect just a bunch of clicks? Most studies on the subject focus on virality as a feature of the content. But what if virality was (also) a feature of the audience? Can the demographics and the structure of the audience of a video explain how it goes viral? And how can you predict virality?
Using Pulsar's content tracking technology we looked at four videos that recently went viral on Twitter: a music video, an advertising campaign, a citizen journalism video and a Vine series. All videos went viral in different ways and whilst there is no simple answer such as a virality formula, the talk reveals the common traits of viral phenomena and how marketers can engineer them in their creative and planning process in order to achieve virality and develop a data-driven content strategy.
PT.1
http://www.facegroup.com/how-videos-go-viral.html
PT.2
http://www.pulsarplatform.com/blog/2013/how-stuff-spreads-how-video-goes-viral-pt-2-the-role-of-audience-networks/
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)
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
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)!
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
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
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)