Archive for the ‘Pulsar’ Category

Today we are introducing a new updated version of the Visibility algorithm that’s powering the Pulsar platform: Visibility 2.0.

The main reason why Pulsar is called Pulsar is that the whole platform is built around the idea of making it easier for anyone to sift through vast amounts of social data by making “important” social media content more “visible”.

One of the key ways Pulsar does this is through its proprietary Visibility algorithm. The algorithm defines “importance” as the ability of a piece of content to reach a larger then average audience and engage a larger than average crowd. The algorithm weights every content on the platform and applies a Visibility score to each post which is then available amongst the metadata used to index and filter the data.

Since we launched Pulsar the Visibility Algorithm has been one of the pillars of the platform allowing you to slice any data view (e.g. trends, influencers, topics) by Volume of data or by the Visibility of the content analysed. Below a series of comparative screens that show how different the same social data looks like when analysed by Volumes vs Visibility:

Posts per Day VS Visibility per Day


Sentiment Volume per Day VS Sentiment Visibility per Day

Top Posts by Volume vs Top Posts by Visibility
But the web is an ever-changing ecosystem: new channels are born, new behaviours are introduced, old behaviours evolve to a new scale or disappear and new ways of measuring them are introduced on a weekly basis. In an effort to keep up with the evolution of the web and continue to deliver effective measures of reach and engagement, over the last three months we have been working hard updating the Visibility algorithm.

The new algorithm takes into account:

  • New sources of engagement data, which are now factored in the calculation of reach;
  • New sources of online viewership data which are now factored in the calculation of reach;
  • New sharing and engagement metrics introduced by the new channels we have integrated, such as Tumblr;
  • Raising levels of engagement across all channels resulting in a need for new engagement and reach benchmarks;
  • New behaviours introduced by new channels like Tumblr, where for example the “weight” of a reaction (a re-blog) is completely different from the weight of a reaction on Twitter or Facebook.

Overall, the new algorithm introduces three key improvements:

  1. More accurate audience size estimates for all channels, particularly for News, Blogs, Forums and Review sites;
  2. More accurate engagement figures across all channels;
  3. A more balanced cross-channel view of reach, to enable effective comparisons between the reach of top down and bottom up media (eg. news vs. tweets).

The new visibility weighting applies from April 10 onwards. However, should you want to re-analyse historical data you can extend the reach of the algorithm to historical data from the Data Management interface in the Results View.

We think the new Visibility algorithm is going to help you run better analysis and make more effective decisions and we look forward to hearing your feedback as you start seeing the new data coming through on the Pulsar platform.

If you are not yet using Pulsar and want to know more about Visibility and Pulsar get in touch here.

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?


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?


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:





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.


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.


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.





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.


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:


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.


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 ( and contact us to arrange a demo – send an email to 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:

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).


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.)


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 ( and contact us to arrange a demo – send an email to and we’ll be in touch in no time.

Or get in touch with the study authors, Jess (@hautepop / LinkedIn) and Fran (@abc3d / 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. We wanted to make sure to share these thoughts on our blog, as well. 

Social media monitoring is a growing industry but one that is stuck in its old ways. And in need a of a urgent re-think.

Since it emerged 15 years ago, the industry has been largely responsible for driving some of the most interesting evolutions in the research space, such as the democratisation of text mining and computational approaches to mining qualitative information.

This is an approach that for the first time enables both a granular and a birds-eye view of the data, making it possible to produce qualitative observations on a mass scale. A new perspective that is blurring the lines between qualitative and quantitative thinking.

And with computation also comes the ability to mine larger (and messier) datasets, which is in turn steadily shifting the focus of what we call knowledge, from understanding causation to identifying correlations.

However, despite the broad impact of social data on the market research industry and the evolution of its infrastructure, the social media monitoring toolbox hasn’t evolved much in 15 years.

While the web, its users, the brands and the advertising strategies have changed dramatically, most social media monitoring platforms today still do exactly what they used to do when the industry first emerged.

The more than 480 platforms currently available on the market all tend to be affected by similar issues: the obsession with shallow volume-led metrics, the inability to measure exposure, the lack of context to the social data, no understanding of the audience and poor data manipulation and visualization interfaces.

Add to this the more systemic revolutions the industry is facing, such as the visualization of social media, which is going to pose huge challenges to an industry that’s been entirely built on text mining.

Most of these issues can be ascribed to the lack of research thinking in the design of the tools we use today. The majority of the companies that established the frameworks in the monitoring space have come at it from a web analytics perspective. Which has lead to favouring the monitoring and analytics framework rather than the insight and intelligence framework for studying online social interactions.

But the analytics approach has also lead to another big misconception. Social data is not quantitative data, rather qualitative data on a quantitative scale. This might sound like a very byzantine distinction to some but over the past ten years this approach has had huge implications on the way social data has been modelled, analysed, sold, delivered and used by organisations worldwide.

Now with social data intelligence becoming central to many organizations and brands, researchers can and have to play a more active role in shaping the tools for the job. So how can we help disrupting a shallow social media monitoring model to make it more powerful and relevant to the way the web works today?

  1. Introducing new ways of sampling social data beyond keyword tracking: Audience Mapping (harvesting content from a set of users) Content Diffusion (harvesting content that contains specific URLs), Social Simulations (agent based simulation based on social data), MROC Augmentation;
  2. Shifting the focus from the content of the conversations to everything around it: context, behaviors, social graphs and interest graphs;
  3. Implementing solid analysis frameworks to move away from basic analytics towards intelligence, for example embedding techniques to make the most of social data such as social network analysis, discourse analysis, reach analysis, attribution analysis;
  4. Opening up the social silo by connecting social data with other datasets such as sales, NPS, stock trading, media exposure;
  5. Introducing scalable human analysis alongside algorithmic coding by crowdsourcing parts of the research process;
  6. Making machine learning ubiquitous to capitalize on the benefits of human coding;
  7. Improving the Data User Experience to support intuitive data manipulation and delivery at different levels of the company and across multiple devices;
  8. Help re-design the company decision-making process: organizations are learning that they have to re-engineer the way they make decisions in order to make the most of real-time intelligence; there’s no point in delivering real-time intelligence if your client can’t make real-time decisions;
  9. Create smart research products based on integrating traditional methods and social data: live segments, social panels, social surveys;
  10. Making Research Programmable: shape the transition from monitoring platforms to social data driven business applications as social data becomes invisible and embedded into the way any organization creates new products, plans advertising or stocks their stores. Move away from dashboards and Powerpoint reports and plug the data and the intelligence into the products and services we deliver.

Time for the researchers to roll up their sleeves.


Found this interesting? Here more from Francesco at our Viral Video Webinar October 23rd when he presents on how different videos go viral differently – and what marketers and brands can learn from it.