How Stuff Spreads: how video goes viral

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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/

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  • But most of these variables seem to correlate

    Based on these correlations, there seem to be two types of memes: Spikers and Growers

    Spiker
    Higher Volatility
    Faster Peak
    Higher Velocity
    Higher Shareability
    Shorter Lifespan

    Grower
    Lower Volatility
    Slower Peak
    Lower Velocity
    Lower Shareability
    Longer Lifespan

    But what makes a meme spread in one or the other way?
  • All the videos we tracked stimulated a similar higher than average emotional reaction within the target audience (52-56/100 Sensum Score). sensum.co
  • sensum.co
  • First thing we looked at is amplification

    Amplification is a measure of the average "visibility" of the meme.

    Where there more influential people in the audience of one of the memes that helped it spread faster?

    Lower for Turkish protests – primarily shared in Turkey, a slightly newer (though still very active) Twitter market
    Lower on Ryan Gosling – perhaps as funny content appeals to a younger audience
    Highest for Dove (video with a message) and Commander Hadfield (tapping into an older Bowie fan audience, plus big influencer RTs)

    Mostly equivalent, so NO.
  • Is globality affecting speed? Does a more global or a more local meme spread faster?

    The answer is again, no

  • So we took to Bayesian statistics to analyse the demographics of the audience

    Does the demographics of the audience affect the way content goes viral?

    There doesn’t seem to be any correlation with demographics as they are completely different

  • We run demographics analysis on the top clusters within each audience and identified higher demographic diversity in Gosling and Dove, and lower demographic diversity in Hadfield and Turksih
  • Virality doesn’t exist as such. It’s a relative concept, totally depending on the target audience
  • And this shouldn’t come as a surprise by now
  • Virality doesn’t exist as such. It’s a relative concept, totally depending on the target audience
  • Virality is 50% content and 50% distribution
  • Great content will spread fast or slow depending on the shape of your audience and how you are leveraging it with targeted distribution
  • Your audience is fragmented into sub communities based on social connections
  • Your audience is fragmented into sub communities based on social connections
  • The broader the appeal of your content the more fragmented your audience is going to be
  • The more fragmented your audience is the more effort you have to put on distribution
  • The more fragmented your audience is the more effort you have to put on distribution
  • Before launching your next campaign, map the communities in your target audience and identifies the hubs by community
  • Mapping the communities within your potential audience is key to find the right trigger, identify the right gatekeepers to escalate the diffusion and have the right strategy in place to support a spiking or a growing meme.
  • How Stuff Spreads: how video goes viral

    1. 1. How Stuff Spreads Francesco D’Orazio, @abc3d Jess Owens, @hautepop pulsarplatform.com
    2. 2. Why do we share?!
    3. 3. Emotion is the trigger
    4. 4. Relevance to our community provides validation (topicality)
    5. 5. Relevance to our community provides validation (timeliness)
    6. 6. Gatekeepers activate the communities within the audience and escalate the diffusion
    7. 7. So given the right content, audience relevance and influencer push, virality should always happen in the same way. Except it never does  
    8. 8. We looked at 4 memes that have “gone viral”: a music video, an ad, a citizen journalism video, a web series  
    9. 9. 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  
    10. 10. 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  
    11. 11. 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  
    12. 12. 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  
    13. 13. @abc3d | PulsarPlatform.com Virality Quantified! Which variables are best for identifying a viral phenomenon?
    14. 14. 15.9m 59m 1.01m L No view count on Views  
    15. 15. 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!
    16. 16. 197%! 194%! 355%! 435%! Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! Coefficient of attention variation (%)! Volatility varies!
    17. 17. 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)! !
    18. 18. 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)!
    19. 19. 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. 20. 20 8 8 2 Dove Real Beauty! Ryan Gosling! Cmdr Hadfield! Turkish protest! Lifespan varies (continuous period at 500 shares/day)!
    21. 21. Although none of the variables alone proved useful to identify a viral phenomenon, all of them correlate around two main models of viral spread  
    22. 22. 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  
    23. 23. But what makes a meme spread along the first or the second model?  
    24. 24. All the videos stimulated a similar higher than average emotional reaction." (52-56/100 Sensum Score / Based on GSR).    
    25. 25. So can the audience composition instead explain why memes develop along one of the other model?  
    26. 26. 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)!
    27. 27. 75%! 63%! 14%! 34%! Globality rate varied! (% of shares from countries other than the top one)!
    28. 28. 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  
    29. 29. 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%! !
    30. 30. 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%!
    31. 31. 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%!
    32. 32. 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%!
    33. 33. 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  
    34. 34. 11.22 6.84 Audience connectedness (avg degree)! 4.26 3.14
    35. 35. Highly connected audiences (higher average degree in the audience network) make the meme spread faster  
    36. 36. 0.506 0.466 Audience fragmentation (modularity)! 0.752 0.650
    37. 37. High audience fragmentation into sub- communities (high modularity of the audience network) makes the meme spread slower  
    38. 38. 130 communities! ! 3 ! connect up to 50% of the audience!
    39. 39. 1356! communities! ! 8 ! connect up to 50% of the audience!
    40. 40. 51! communities! ! 2! connect up to 50% of the audience! !
    41. 41. 382! communities! ! 5 ! connect up to 50% of the audience! !
    42. 42. 130! communities! 51! communities! ! 1356! communities! 387! communities!
    43. 43. But what is causing higher or lower fragmentation within an audience?  
    44. 44. 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
    45. 45. High demographic diversity correlates with high modularity and slower meme velocity  
    46. 46. So, what’s the point?!
    47. 47. There is no such thing as “virality”  
    48. 48. “Virality” is a relative concept depending on the audience of reference  
    49. 49. “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  
    50. 50. Great content spreads fast or slow depending on the shape of your audience and how you are leveraging it with your distribution strategy  
    51. 51. The audience you are trying to reach is fragmented into sub-communities of age, profession, interest  
    52. 52. Using network analysis you can identify these communities by mapping the social graph of your target audience  
    53. 53. The broader the appeal of your content the more fragmented your audience is going to be  
    54. 54. The more fragmented the audience, the more targeted the distribution needs to be  
    55. 55. 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  
    56. 56. So if you want your content to go viral, don’t just put the video out there and see what happens…
    57. 57. 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)  
    58. 58. Thank You! Francesco D’Orazio, @abc3d Jess Owens, @hautepop pulsarplatform.com

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