The power of interest graph analysis

When tasked with analysing the likes, habits, and trends of a group, our go-to methodology is usually to consider each individual’s personal inclinations and extrapolate those to say something broadly about the group as a whole. We see this in opinion polls, in user surveys, and sometimes, in social analysis.

Reasoning about a group in this way isn’t inherently problematic; it’s worked very well for a long time. In the advertising and marketing world, persona-driven campaigns have proven to be an enduring and impactful strategy. Besides—we’re naturally good at this. Humans love to label people, and groups: codified language like Millennials and Baby Boomers pack a lot of information into a conveniently generalised term.

Aggregated information about individuals’ traits (e.g. “How much money do you make?,” “How old are you?”) is often turned into peripheral observations when developing personas: “Tends toward upscale shopping habits” or “Decisions motivated by the need for adventure.” While comfortable, these broad generalisations are arrived at often by gut feelings about which users’ responses make up a meaningful trend among the whole group.

This is all to say: trends are supposed to be harmonious, complementary, and provable. A greater-than-the-sum-of-their-parts approach to defining personas is ultimately not very scientific, even less so when the parts themselves are basic demographics like age, gender, or income. New methods for building personas are driving label-and-demographic approaches to obsolescence.

So, if broad categorisation is becoming obsolete, what’s the alternative?

The alternative is the network. Social media analysis and machine learning let us look at the connections between individuals, evaluate them, and reason about them in aggregate. Network science phrases things in terms of ‘betweenness centrality’ and ‘node influence'; we use these plus other observations to say things like: “this set of users know one another very well,” or “they like the same sorts of things.” Interesting and complex dichotomies arise when we look at these kinds of attributes: what was once a two-dimensional list of people is suddenly a many-dimensional interest graph.

It turns out that segmenting and clustering these networks proves to be totally enlightening. Algorithmically-defined clusters tend to be much richer than comparisons using plain demographic labels. Maybe this should come as no surprise: machine learning lets us consider lots of (many millions, actually) interest variables when comparing individuals from a group.

Instead of a persona driven by things like “People who live in London and like football,” we can cluster on metrics like “People who follow @Arsenal and @SkyFootball but not @EnglandCricket or @SkyCricket,” and millions of permutations thereof, shockingly quickly.

As for the effect that this has on making persona-based decisions in marketing, advertising, branding and product development: you now have the ability to dive deep. Generalisations and user-provided labels tell a very shallow story. Likes, trends, and interest patterns get at the silent heart of social behaviour: trends are harmonious, complementary, and quantifiable.

Following patterns to get at the silent heart of social behaviour

One of the reasons we consider following patterns to be paramount to social analysis is that interacting (i.e. following) with other people is a fundamental property of social behaviour. Identify your friends and interests, and establish a relationship with them: this is the minimum act that must take place on networks like Facebook, Twitter, and Instagram.

This is not done out loud, where social biases might keep individuals from being honest about what they talk about in person (While I do follow some political candidates, good luck finding a tweet where I talk about my political leanings.); rather it is done silently and by default while building your personal network.

These silent patterns play a role in analysis at many levels, but to demonstrate their importance, consider the following: 61% of online Millennials get their political news on Facebook, compared to 37% from TV. This is big: it means that young people get a say in what influences them, since they follow/friend people of their own choice.

In short, when building their network, people also build an interest graph. That interest graph can be analysed and monitored for quantifiable trends, the depth of which provides a much more holistic picture of any social behaviour than polling or demographic insights alone are able to do.

Phil Renaud is founder and vice president of engineering at Affinio