Archive for the ‘Blog’ Category

In the first part of this blog series (How Stuff Spreads | How Videos Go Viral part 1: Models of Virality) we identified 7  dimensions that describe and quantify virality. Although none of the variables alone proved able to define a viral phenomenon on their own, they correlated into two models of viral diffusion. One model we called “spike” – the sudden ‘explosion’ of sharing activity – and the other we called “growth”, where popularity is a slower and steadier grower.

Spikers vs Growers

In this blog post, our Chief Innovation Officer and  VP Product Francesco D’Orazio using social network analysis looks at how the audience composition and structure influence the way video spreads.

What makes a viral video spread in one or other of these ways? Most studies on the subject  focus on virality as a feature of the content. But what if virality is (also) a feature of the audience? Can the demographics and the structure of the audience for a video explain how it goes viral?

To recap, we were studying 4 videos:

In this blog post we will show how we analysed the demographics and the social network properties of each video’s audience to understand better how they spread.  Read on for some of our best network graphs yet and some fascinating findings…

Metric #1: Amplification and influence

The first thing we looked at is Amplification. Amplification is a measure of the average “visibility” of the tweets carrying the meme. Tweets with higher visibility imply a more influential status of the author who posted them. Can the influence level of the audience of a meme explain its slower or faster diffusion?

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Amplificiation is similar across all audiences. It’s fractionally lower for the Turkish protest video and for Ryan Gosling, the first primarily shared in Turkey, the second appealing to a slightly newer (though still very active) Twitter audience. And it’s slightly higher for Dove Real Beauty Sketches and Commander Hadfield. In both cases the variation doesn’t correlate with the virality model of the meme.

Metric #2: How international were the audience?

So the next hypothesis to explain the velocity of the memes was the geographic distribution of the audience. We quantified this as Globality: the percentage of meme shares coming from countries other than the main country. So does the “internationalness” of a video affect its virality? Does a more global or a more local meme spread faster?

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The answer is again, no. The Turkish protest video was “local” but so was Ryan Gosling – and one spread instantly but the other peaked on day 18. 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 video.

Metric #3: Demographics.

Does the demographics of the audience affect the way content goes viral? Do young, techie male students from global cities push a meme faster than, say, middle aged housewives from rural Germany?

We used Bayesian statistical inference to analyse the demographics of the audience. This method uses the available information on Twitter and matches it to a sample audience interviewed in real life to get known demographics, across the various countries involved in the study. Below is a summary of the most prominent demographics traits of the four audiences:

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Although students and global cities feature heavily in all audiences, there doesn’t seem to be any direct correlation between virality models and demographic traits. Instead the demographics are completely different for each meme. Not to mention that students represent 33% of the Ryan Gosling audience, the slowest meme of all – so it seems that youth demographic probably isn’t necessarily a critical cause for a video to go viral quick.

And now some Social Network Analysis…

The audience gets more interesting when you start to look at its social structure. As we couldn’t find any correlation between demographic traits and virality models, we turned to the structure of the audience by mapping the social graph of the people who shared the video.

Your ‘social graph’ is the network of the people you know, and how they’re connected to each other. Because we were studying Twitter sharing of videos, we had easy access to this data through two variables: who each video sharer was following on Twitter,  and who they’re followed by. In technical terms, this gives us a ‘directional’ network with two possible ways for nodes to be connected.  Analysing these connections highlighted some really interesting differences.

Metric #4: Degree, or ‘social connectedness’

First of all we looked at the Average Degree of each audience network. Each person in a network can be assigned a ‘degree’ value: that’s a count of the number of connections they have to other people in the network. We were studying how videos spread in Twitter, so those connections are easy to identify: it’s who they’re following and who they’re followed by.

Interestingly enough, the audiences of the Spiker memes (Commander Hadfield and Turkish protests) are showing the highest levels of interconnectedness – while the audiences of the Grower memes (Dove Real Beauty and Ryan Gosling) show the lowest.

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Metric #5: Modularity, or “fragmentedness”

The memes that spread faster could do so because the audiences that engaged with them were highly interconnected. But how are this connections organised? To do this we used another social network analysis metric called Modularity. This describes how fragmented the network is and how many sub-communities can be detected based on the density of mutual social connections within clusters of users.

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The lower the modularity, the less fragmented the audience is into sub-communities, the more cohesive it is and the easier to reach it is. Not surprisingly, the audiences of the Spiker memes are the most cohesive ones, while the audiences of the Grower memes are the most fragmented ones. Cohesiveness and fragmentation becomes much easier to understand when looking at the total number of communities identified within each audience.

Metric #6: the number of Communities

Social network analysis tools allow you to measure the number of ‘communities’ in a social network. Tools such as Gephi provide access to algorithms, such as the Modularity one, that can quantify how people’ s connections tend to gather together into definable ‘clusters’ of closely-connected groups.

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Whereas the audience of Commander Hadfield is split into 130 communities and the audience of the Turkish protests is split into 51, the audience of Ryan Gosling is split into 382 communities and the audience of Dove Real Beauty Sketches into 1356.

This has a strong impact on the ability of memes to spread through the audience network. Whereas reaching out to just 2 communities is enough to reach 50% of the audience of the Turkish protest, spreading the news to 50% of the audience of Dove Real Beauty Sketches requires reaching out to 8 communities. It follows, then, that where a meme has to travel through more communities to reach people, it moves a little slower – in a ‘grower’ model. By contrast, memes ‘spike’ where they take off in a small number of communities very quickly.

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So what have we learnt so far?

Yes, the audience’s social structure – the way that people are connected within in – shapes the way something goes viral.

Audiences with a low Average Degree, low connectedness or low density, are more fragmented. The more an audience is fragmented into sub-communities (high modularity of the audience network), the slower a video or piece of content spreads through it . But what causes a higher or lower fragmentation within a specific audience?

Understanding the communities within an audience

To answer this question we tried to measure the demographic diversity of the audiences. The assumption being that an audience showing a higher demographic diversity will also be more fragmented and therefore slower to transmit viral videos.

So we ran the demographics analysis again on the four audiences: this time running it separately on each of the top 5 community clusters identified within each audience. You can see below the results for the top 2 clusters of each audience:

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The audiences of Ryan Gosling and Dove Real Beauty Sketches show higher demographic diversity, while the audiences of Commander Hadfield and Turkish protests show lower demographic diversity. So high demographic diversity correlates with high modularity and slower meme velocity after all. How is this useful?

How the audience affects How Stuff Spreads

To start with, this means that a meme which is appealing to a broad demographic is probably going to spread slower than a meme that is appealing to a narrow demographic.

This also means that a meme with a broad demographic appeal is going to be more expensive to make go viral. Expensive because it will require more intense paid for seeding/advertising in order to reach out to a higher number of disconnected communities (Dove Real Beauty Sketches is a good example). It may also need persistent replication of the meme to break through the attention of multiple audiences who might not take notice the first time (Ryan Gosling won’t eat his cereal is a good example).

Finally, the organisation of the audience in sub-communities means that influencers lists by subject are pretty useless when trying to reach out to an audience. For example, your top 100 influencers for beauty might well all be part of the same two communities out of the 1356 total communities that make the Dove audience. So identifying gatekeepers and influencers is useful only once the audience you want to reach has been mapped and its communities identified.

The social dynamics of virality

In our previous post we identified and defined two models of virality: Spike vs. Growth.

From this audience and community analysis, we can now augment that with a 3-part model of how content is seeded through groups of people:

1) TRIGGER: A higher than average emotional response to the content triggers an impulse to share

2) VALIDATION: The impulse to share gets then validated against the community the user is part of. This validation happens both in terms of topicality (is this of interest to my audience?) and timing (has anyone else already shared this within my circles?). See this paper for more research on this aspect

3) ESCALATION: The gatekeepers (e.g. media channels, celebrities etc) share the meme helping it reach the tipping point within a specific community. The tipping point is when every member of the community is likely to receive the meme from another member of the community.

Once everyone’s seen the meme and starts to share it on themselves… That’s when you’ve got virality on your hands!

 So what does this mean for you?

Content that generates an emotional reaction is more likely to go viral. People share to say something about themselves. Emotional content helps them figure out easily what it is they are saying about themselves by sharing it.

Picture the audience your content is going to be appealing to, and find them in social media. Learn who they are and what makes them tick.

Your online audience is not a monolith. Online audiences are organised in sub-communities and congregate around key demographics variables such as age, profession, passions and interests.

Map your audience and identify the key communities that are going to help you reach out to at least 50% of your audience.

Once the communities are mapped,  identify the key gatekeepers by community and the connectors between key communities. This will help you reduce the outreach effort.

If your content is going to appeal to a broad demographic expect a longer run and make sure you have the right resources in place for seeding and advertising to a fragmented and  harder to reach audience.

Good luck.

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Previous posts in this series:

Found this interesting? Got viral content of your own that you want to understand? Check out the tool we used for this study, Pulsar (PulsarPlatform.com) and contact us to arrange a demo – send an email to James.Cuthbertson@Facegroup.com and we’ll be in touch in no time.

Or get in touch with the study authors, Francesco D’Orazio (@abc3d /LinkedIn) and  Jess Owens (@hautepop / LinkedIn).

In an article recently published in Research World Magazine and on his Tumblr blog Abc3d, our Chief Innovation Officer, Francesco D’Orazio outlines the challenges facing the social media monitoring industry – and 10 ways to tackle them.

Following the article Francesco has been invited to present at the MRS Social Media Research Summit  in London and at the Researching Social Media Conference in Sheffield, you can find the full presentation here:

Marketers all want to create content that people will pass on. There’s a lot of talk and research about what “shareable content” looks like and why one piece of content went viral while another piece didn’t. But most of these studies focus on the content, assuming that something about one piece of content makes it more shareable than another.

But the real question is: Why do we share at all? What drives us to press that “Share to Facebook” button? By turning our attention to the people doing the sharing, we can understand the drivers behind sharing in the first place. Once we do that we can then tailor content to better satisfy those needs.

In this 30 minute webinar, we will begin to answer the question of why people share. We will cover:

  • The types of content that is already being shared using data from our Harlem Shake vs Gangnam Style and Viral Videos research
  • What needs that content fills
  • And how you can improve your own content strategies armed with this understanding

Register Here for the Why We Share Webinar

December 11, 2013

11am EST/4pm BST

Twitter viral video - network maps

Webinar Speaker

Kate Davids is a Senior Researcher and founding member of the New York Face offices. She is fascinated by how social data tells us as much about ourselves as it does about opinions of brands. Her marketing background – she holds a Masters in Digital Marketing – helps her translate insight from social media analytics into actionable marketing strategies.

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Read the inspiration for this webinar, the blog “Why Do People Share Content in Social Media” or contact Kate on Twitter @KateDavids or on LinkedIn in/katedavids.

Wouldn’t it be great if brands could just sit in while a woman did her makeup, listen to her complain about what’s not going right and see her smile when she gets the look she wants? Thanks to social media, they can do just that.

Woman putting on makeup

[image by Flickr user Mustafa Sayed]

In his blog “3 new use cases for social data in research” our COO, Job Muscroft, talked about how smart social media research, done right, can help develop new product innovations. People are sharing their experiences with products and services daily, allowing us to observe the thoughts of people shopping for groceries, cooking, commuting to work and hundreds of other activities. As soon as a new product hits the shelves, consumers are providing feedback on it. And through using social media listening tools, this feedback can be fed right back to the product teams to help with improvements.

In this blog, I’ll lay out the advantages of using social media for innovation, and explain how brands can go beyond listening to individual mentions to finding the new ideas and insights that can help them build their products.

Going Beyond Monitoring

Using social media for innovation is different from the customer service monitoring that many or most brands are doing now. Now it is not just about helping customers when they are unhappy. It’s about figuring out the root cause of their unhappiness and fixing it in the next edition. This a deeper form of research that endeavors to reveal wants, needs, pain points and motivations by looking at behavior around current products.

This is already being done through focus groups and customer service surveys, but social media is immediate and intimate. As soon as a product or service is released, people can be responding and talking on social media. Moreover, they are sharing this information as they use the product in their regular lives. These aren’t prompted trials. These are spontaneous and organic responses. Researchers really can, in a way, watch a dad cook dinner for his kids. This allows researchers to examine unspoken needs and frustrations as they listen not only to what people say about a product, but also how they use it.

From Many Mentions to One Innovation

At its heart, using social media for innovation is qualitative research. Numbers and quantitative metrics do come into play – the data is just too huge to not rely on some form of numerical metrics. But we try to get at the numbers in a way that recognizes the unstructured nature of human language.

The first step is for the researcher to read a bunch of social media messages, just like a qualitative researcher would read the verbatims from a community or focus group. From here, we can develop a code frame of different scenarios and use-cases that we can group the messages into.

[image by Flickr user Seth Woodworth]

Next, we build a lexicon to help us sort the mentions into our code-frame. A lexicon is the keyword filter we use to examine our data and find relevant mentions. For instance, a lexicon might include “blades” and “knives” for a search about a food processor to narrow in on discussions about chopping power. Then we can compare the volumes between the different scenarios to find the most pertinent areas of discussion.

Finally, we do more reading and analysis in order to identify the common themes and patterns that are driving these mentions. Here, tools like the Bundle visualization in our social media research platform Pulsar come in real handy. They help us see the patterns in the language, which we then test by going back to the actual search mentions, applying a level of qualitative rigor to our analysis.

White-Space Innovation

This research method can help develop new products as well as innovate around existing products. Social media searches can gather data on product categories, consumer needs, issues, and even competitors just as easily as finding mentions of a particular brand or product. The only thing that changes in the research process is the original search we run to find the dataset of mentions and behaviors to analyze. For innovation around a specific brand or product, we would craft a rather specific search string.

But when doing a white-space innovation project, it is better to cast the net widely and look for all mentions of the category or interest area. This allows us to adapt to the way people actually speak, finding the relevant mentions we might otherwise have missed. Other options are to look for verbs and activities to see how people are currently achieving desired results.

This is what we did for an FMCG client recently, who was looking to come up with new platforms for product development across a range of haircare brands. How had women’s hair needs moved on since the era of GHDs and poker-straight locks? What were the emerging styling trends, information sources, and the language used to discuss these looks?

Honing In On Your Target Audience

Whether the goal is to innovate around an already existing product or develop ideas for new products, the first step is to figure out what words to mine the internet for. This is of crucial importance as this initial search will provide all the data for the following analysis. The search string must reflect consumer language or you simply won’t get back anything useful.

However, sometimes this kind of keyword tracking search is too general, bringing back results from people that you aren’t interested in. If your product is targeted at college kids living away from home for the first time, then you want to hear about that market’s frustrations with doing laundry, not what their mothers are saying.

[image by Flickr user Brian Ingmanson]

This is when we would layer on an audience search, looking at just your target audience. An audience search allows us to sample the online population by behavioral or demographic traits. That way we can be sure that the data we are analyzing comes from the relevant market. The downside of this is that it can be limiting – you only see mentions from the people you are sampling. On the plus side, all those mentions are relevant to the market you are looking to innovate in.

Beyond allowing companies to better tailor their products and services to consumer needs, this form of social media analysis offers a competitive advantage as well – speeding up the innovation cycle. As soon as a product is released, brands can begin looking for improvements. As soon as a new trend appears in the magazines, brands can start looking for ways they can jump in. Companies using this kind of social intelligence will be set up to win, moving faster and more strategically than their competitors.

Social media research offers us new ways to observe and understand consumers. In a way, social media is a window into their homes, revealing how they live with the products companies produce. By looking at this, understanding trends and the consumer experience, we can help companies produce better, more targeted products. Everyone wins – consumers get products that actually do what they want them to do, and companies get products with a waiting market.

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For more more new use cases for social media research read “3 New Use Cases for Social Data in Research.

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 (May 2013), Francesco D’Orazio and I have been working with Twitter UK to explore four more big viral phenomena. The stories we selected have all been driven by video, and have been chosen to represent various types of video content:

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 this series of blog posts. But first, take a look at the diffusion maps below, which show the pattern of tweets and retweets for each video (click to embiggen).

Twitter viral video - network maps

(Blue nodes = tweeters. Yellow nodes = retweeters. Size = author visibility, i.e. estimated reach).

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. 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.

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.

Twitter viral video - Twitter size

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.

Twitter viral video - YouTube views

But we also believe the different viral patterns of the two videos can explain this discrepancy. Read on for details…

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.

Twitter viral video - Commander Hadfield Space Oddessy Twitter viral video - Dove Real Beauty Sketches Twitter viral video - Turkish protest Izmir] Twitter viral video - Ryan Gosling Won't Eat His Cereal

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:

Twitter viral video - days for sharing to peak

It’s worth “zooming in” on this peak day to understand this maximum rate of sharing better.

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.

Twitter viral video - Velocity Twitter shares per hour

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 video is of course one we first observed in our Gangnam Style vs. Harlem Shake study back in May. 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 stay show a steadier pattern of interest?

Stats-heads among you will 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 mean (average) number of daily shares. This gives us a % figure.

Twitter viral video - Variability

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 evenly-dispersed 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.

Twitter viral video - retweetability

So there’s some variation: The Dove video generated the most original tweets, in about a 3:2 ratio with retweets. 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 CSA”
  • “@Cmdr_Hadfield 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 of @TheFunnyVines, 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.

Metric #7: Social Currency

The final dimension we want to talk about in this post 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 blog post. 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 it is to their friends & followers. And of course sharing is also a representation of self: people share content that makes them look good. We discuss this in more depth on our blog post Why We Share.

Twitter viral video - social currency

So what was the social currency of our viral videos? A surprise leader: the Turkish protest video, documenting police violence in Izmir.  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 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 big post 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:

Twitter viral video - conclusions 2 models of viral spread

In our next blog post, Face CIO and Pulsar creator Francesco D’Orazio will talk about the people who made these videos go viral. Who were they – which demographics did each video reach? And how does content spread through online communities?  Does “Spike” virality travel through communities differently to slower-burning “Growth” virality? Watch this space!

(Or, as a preview, watch our videos of how this content diffused through influencer hubs, over on the Twitter blog.)

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How Videos Go Viral Part 2 on how audience networks shape viral dynamics is now published – read it here.

Found this interesting? Got viral content of your own that you want to understand? Check out the tool we used for this study, Pulsar (PulsarPlatform.com) and contact us to arrange a demo – send an email to James.Cuthbertson@Facegroup.com and we’ll be in touch in no time.

Or get in touch with the study authors, Jess (@hautepop / LinkedIn) and Fran (@abc3d / LinkedIn).