WHITE PAPER! How Stuff Spreads #2:! How Videos Go Viral Part 1!

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1 WHITE PAPER How Stuff Spreads #2: How Videos Go Viral Part 1

2 How do videos go viral? How do people share them through social networks? And what are the dynamics of virality? Following the success of our Gangnam Style vs. Harlem Shake study, Francesco D Orazio and Jess Owens have been working with Twitter UK to explore four more big viral phenomena. The stories selected have all been driven by video, and have been chosen to represent various types of video content: Commander Hadfield singing Bowie s Space Oddity on the International Space Station (music) Dove Real Beauty Sketches for advertising (the most-watched advert EVER on YouTube) Ryan Gosling Won t Eat His Cereal series of Vine videos, for serialised narrative content and mobile A grass-roots video of June s protests in Izmir, Turkey, to provide an international and news dimension Turns out there s not a single model of virality. Instead, different types of videos spread in different ways. Different types of content appeal to different audiences and the structure of these audiences is what shapes the viral diffusion. Understanding the dynamics of that spread quantifying it using metrics, and digging into the influencers and demographics to understand some of the how, is what we re going to talk about in the How Stuff Spreads series. But first, take a look at the diffusion maps on the next page, which show the pattern of tweets and retweets for each video. Info about image

3 It s immediately clear that there s something different going on for each. Some, like Commander Hadfield, have one big hub (Hadfield himself) driving half or more of the sharing. 5% 1% Blue nodes = tweeters. Yellow nodes = retweeters. Size = author visibility, i.e. estimated reach Info about image

4 Others like Dove Real Beauty and the Turkish protest video show a constellation of many smaller influencers, each being reblogged by smaller groups. Read on and we ll explain why. 5% 1% Blue nodes = tweeters. Yellow nodes = retweeters. Size = author visibility, i.e. estimated reach Info about image

5 What we did We used Pulsar s content-tracking technology to collect and analyse any tweet containing a link to the videos we were tracking. What we re analysing is content diffusion and content discovery the way videos are shared, recommended, and retweeted until they become viral phenomena. Of course people share content in other ways too not least on Facebook and YouTube search is in fact the second biggest search engine in the world (after Google). But Twitter provides the strongest dataset for analysis, and its role as a hub for curating content from across the whole social web makes it an apt case study. Metric #1: Size At this point the results are straightforward the Canadian astronaut wins, with an audience of 75,000 sharing his video on Twitter. Space oddity has a wide, even global appeal in contrast to our smallest video, the Turkish protests, which was shared by just under 12,000 people on Twitter, very largely within Turkey (see next page). What s interesting is how this contrasts with the YouTube view counts, shown in the chart below. Commander Hadfield may have got more Twitter sharing but Dove Real Beauty Sketches got nearly 4x as many YouTube views. Now, there are a number of reasons for this, not least sharing on platforms other than Twitter. Is the affirmative, personal message of the Dove advert something people prefer to share with their Facebook communities of real world friends and family? Whereas Hadfield has a science and news-y angle that is more suited to Twitter? Quite possibly. But we also believe the different viral patterns of the two videos can explain this discrepancy. Info about image

6 Info about image The active Twitter audience of each video

7 Info about image Total YouTube video views of each video

8 Metric #2: Sharing over time The following charts tell the story of how each video was shared over time. Note the similarities and differences: while all of them essentially have sharp spikes in sharing (it s that buzz of thousands of shares per day that made them viral in the first place), there are different patterns. 5% 1% Info about image Shares over time for Commander Hadfield

9 Shares over time for Dove Real InfoBeauty about image

10 Shares over time for Turkish Protests Info about image

11 InfoCereal about image Shares over time for Ryan Gosling Won t Eat His

12 So there seems to be two patterns: 1. Two videos (Commander Hadfield and Turkish protests) peak immediately, in the first 24 hours from launch. 2. The Dove and Ryan Gosling videos, by contrast, show a more sustained level of buzz over 20+ days. There are still spikes, sure but interest is much less front-loaded than it is for Hadfield or Turkey. Metric #3: Days to Peak So we might bring in another metric, Days To Peak. How many days does it take each video to hit its maximum sharing rate? This splits our set of videos in two: two of them peak on launch day (first 24 hours), whereas the other two take several days to get to maximum velocity: Days for Twitter sharing to peak Info about for each image video

13 Metric #4: Velocity The chart below shows the Twitter shares per hour for each video, and the results are really interesting: the 2 videos that peak on Day 1 (Hadfield & Turkish protests) don t just peak on Day 1, they actually peak on Hour 1 or Hour 2. This shows just how viral this content really is it gets thousands of people s attention instantaneously, and is sufficiently powerful for them not just to watch the video, but for thousands of people to tweet and share it as fast as they can. 5% 1% Info about image Velocity: Shares per hour on peak day

14 It s also interesting that the Turkish protest video gained only about 6% of Commander Hadfield s YouTube audience, but nonetheless reached the same peak sharing rate: 6,000 shares per hour. This shows how much of an impact timely, relevant news stories can have in a smaller community. So if you re seeing what we re seeing, there are now clearly two patterns: Spike: Where a video explodes into social with a big bang, getting attention immediately but then burning out quickly Growth: The slower-growing version of virality, where a video gets picked up by influencers and introduced into new communities over many days This pattern for viral videos is of course one we first observed in our Gangnam Style vs. Harlem Shake study. The Gangnam phenomenon was a grower that kept running for over six months, whereas the more celeb-driven Harlem Shake showed a spike pattern, bursting up and then dying down again quickly. It s all very well to describe these patterns visually, but what if we wanted to quantify this so we could compare it objectively? Introducing our next metric: variability. Metric #5: Variability This was a metric that took a bit of thought. We wanted to find a way to quantify the spikiness of our video distributions. Which ones have the most extreme spikes on their peak day? And which ones show a steadier pattern of interest? You might be familiar with standard deviation, the measure of how much deviation or difference there is within a series of numbers. Our variability metric is a normalised version of this: the coefficient of variation, aka the standard deviation of Twitter shares per day, divided by the average number of daily shares. This gives us a Info about image percentage.

15 Variability for each Info abut video image

16 The first thing to note is that all the videos show a high level of variability. We ve seen this on the Twitter shares over time charts already all of them have substantial spikes in interest, of varying degrees of steepness. Social virality is never an entirely evenlydispersed phenomenon as we d expect, for something traversing the power-law distribution of social influence. For comparison, the O2 brand shows 71% variability in day-to-day discussion, and Tesco 47% (October 2013 figures). So you can see the Twitter diffusion of these videos was much spikier than typical topic buzz. But looking at variability, we also see our spike vs. growth model confirmed. The two spike videos, Commander Hadfield and the Turkish protest both display around twice as much variability as the grower videos, Dove Real Beauty and Ryan Gosling. So variability s a really useful measure for identifying which kind of virality you ve got on your hands. Metric #6: Retweetablity Virality isn t about people seeing things, it s about people doing things sharing. And on Twitter that can take two forms: original tweets sharing the video URL, and retweeting other people s messages. We find it interesting to examine the ratio between the two, as there s a surprising amount of variation. The chart below shows how many retweets each video-sharing post got. You could call this the engagement rate, but to our mind it s only one measure of engagement. People s original tweets sharing a video are another form of engagement, one that might even be more valuable for a brand as it carries a greater sense of personal advocacy. So there s some variation: The Dove video generated the most original tweets, in about a 3:2 ratio with retweets. Info about image

17 5% 1% Retweets per original post ratio for each video

18 Examining the messages themselves, we see a lot of personal comments being added wow, wow, wow so powerful. THIS IS A MUST WATCH FOR EVERY WOMAN and every man who loves one This made me cry, as women we have to be more kind to ourselves. Commander Hadfield also generated more original messages than retweets (1 to 0.89). The tone of people s messages was different to Dove however compared to the sense of I relate generated by Dove, here people were simply awestruck by something out of this world Chris Hadfield is the boss of bosses. Really interested in seeing what he does after the gives an amazing look from space. This time, Bowies Space Oddity. Absolutely incredible. I m aware I ve linked this before. I do not fucking care. It is absolutely epic. But the real finding of our retweetability metric is just how retweetable the Ryan Gosling Vines were. They gained fully 4.3 times as many retweets as original posts. This isn t about maintaining attribution to the author, as most retweets were not creator Ryan McHenry. So what s going on? Across a wide range of categories we see humour getting retweet rates an order of magnitude higher than other stories. Perhaps the impulse to retweet a joke is a fleeting one, making pressing retweet more appealing than copy-pasting and typing out for a original post. Alternatively, with Vines being a new-ish format, perhaps people may retweet as then they know that the Vine will show up properly. Info about image

19 Metric #7: Social Currency The final dimension we want to talk about is social currency. We define this as Twitter shares per million YouTube views. This isn t a measure of popularity per se that d be the YouTube views total, which we ve shown at the start of this white paper. Dove won that contest, with a massive 59 million views. Instead, social currency can measure the social value of a piece of content how far people think it s relevant to their friends & followers. And of course sharing is also a representation of self: people share content that makes them look good. So what was the social currency of our viral videos? A surprise leader: the Turkish protest video. 5% 1% Info about image Shares per 1m views for each video

20 This achieved a massive 12,900 shares per 1m YouTube views, 2.5x the Commander Hadfield performance, and 11x Dove Real Beauty Sketches. What do we think was going on? Well, unlike the other two, the Turkish protest video was news content. It showed protests and the state clampdown spreading from beyond Istanbul into other cities (Izmir), and potentially citizens with camera-phones were able to provide the first record of this event before official news crews got there. So this story spread like wildfire within Turkey, hitting a velocity of 6,000 shares/hour. By contrast, the Commander Hadfield and the Dove videos were both a little less urgent. They were both bigger overall meaning there s more chance your friends would already have seen them via other people. So that factor, overexposure, could dissuade sharing and reduce social currency. We also hypothesise that the Dove video may have gained more sharing on Facebook rather than Twitter, given its more personal message. (We would love to measure this too but with a large and unknown percentage of private data on Facebook it s less easy to do so). Conclusions This is a white paper with a lot of variables and data. So let s recap on what we re saying overall. How do viral videos spread socially? We can see there are 2 broad patterns of content diffusion. One model we call spike the sudden explosion of sharing activity and the other we call growth, where popularity is a slower and steadier grower. The metrics we ve discussed, such as velocity, variability and social currency, provide a way to identify which kind of virality you re looking at: Info about image

21 Info about image

22 About the authors Francesco D Orazio - VP Product at Pulsar Francesco is a researcher and technologist with a social sciences and digital media background. He explores new opportunities emerging at the intersection of technology and research. Francesco designs systems and methodologies that help analyze the social web and extract insights in real time using computational social science and data visualisation. He is a regular speaker at research, innovation and technology conferences. His work has been featured in the likes of Ad Age, BBC, Guardian, CNN, The Independent, Mashable, Gigaom, Business Insider etc. Jessica Owens - Social Media Research Manager Jessica completed a BA in Anthropology at LSE and a research Masters at UCL before deciding to swap the study of remote Papua New Guinea tribes for commercial research into the equally exotic UK consumer. She joined Pulsar in 2010 to follow her interests in social media, technology and online research methods. Jessica now works at the intersection of qual and quant research doing real-time web and social media research for our clients.

23 THANK YOU If you want to find out more about Pulsar please book a demo by ing us at: info@pulsarplatform.com