How Audience Structure Impacts Content Virality
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1) The document analyzes four viral videos or memes to understand what factors contribute to something going viral. 2) It finds that variables like views, shares, and engagement alone do not determine virality. Instead, content spreads either as "spikers" with fast, high peaks or "growers" with slower growth over a longer period. 3) The structure and fragmentation of the target audience impacts which spread pattern occurs, with more fragmented audiences correlating with slower, grower-type growth. Understanding audience communities is key to effective distribution strategies.
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How Audience Structure Impacts Content Virality
- 1. How Stuff Spreads Francesco D’Orazio, @abc3d Jess Owens, @hautepop pulsarplatform.com
- 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
- 20. 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!
- 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
- 41. 11.22 6.84 Audience connectedness (avg degree)! 4.26 3.14
- 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 Jess Owens, @hautepop pulsarplatform.com
Notes de l'éditeur
- 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.