Last time we took a quick peek at the history of SEO, and we saw that influence engine optimization (IEO) is an inevitable consequence of scoring people’s influence. What’s worse is that IEO leads to the influence irony, where it actually changes people’s behavior in a way that drives them further away from being truly influential (if you missed this crucial point from my last post, you should read The Influence Irony – Influence Engine Optimization).
This sounds disappointing, but today, we are going to fix it!
In my previous writing on digital influence, we had a rather scientific and statistical discussion about validating algorithms which predict people’s influence. When you dig deeper into what influence vendors actually do to validate their algorithms, you quickly find that most influence scores cannot be trusted. Mainly because vendors don’t validate, overgeneralize, or validate their algorithm using flawed circular logic.
Another serious problem with most influence scoring models is “IEO.” You see the title; I really meant influence engine optimization (IEO) as opposed to search engine optimization (SEO). What is IEO? That will be the topic of discussion today and I promise it will be much less technical than my last post.
Last time, I illustrated the predictive validation framework in a toy problem where we are supposed to predict the stock price of Apple. Today we will apply this framework to analyze algorithms that compute people’s influence score. Since this is second part of a two part article, you will need a solid understanding of the first post in order to make sense of today’s discussion.
To validate any model that influence vendors use to predict someone’s influence, they must have an independent measure of that person’s influence. But as we discussed before, nobody has any measured data on influence. So how can influence vendors be sure of the validity of their model?
This post is the first of a two part article addressing the question: How do you know if your influence score is correct? Today, I won’t actually answer this question, but will show you a step-by-step procedure that we will use next time to address this question.
Because nobody actually has any data on influence (i.e. data that explicitly says who actually influenced who, when, where, how, etc.), all influence scores are therefore computed from users’ social activity data based on some models and algorithms of how influence work. However, anyone can create these models and algorithms. So who is right, and who has the best model? More importantly how can we tell and be sure your influence score is correct? In other words, how can we validate the models that influence vendors use to predict people’s influence?
Last time we explained why nobody can actually measures real influence. So influence vendors must build models that predict someone’s influence in order to compute their influence score.
The problem is that most of these influence models focuses on the influencer. Nearly all models are focused on estimating the influencer’s social capital. Therefore an influence score is merely a prediction on the influencer’s potential to influence.
Since I’ve decided to rotate more frequently between different projects that I’m working on, I feel this is a good time to revisit the topic of influence and pick up where I left off on this fascinating subject.
It’s been more than two years since I wrote about influence. You can find my chapter plus miscellaneous articles on influence via the label under “influencers”. I didn’t lose interest in this subject – the simple fact is empirical research takes a lot of time, much more than qualitative research. I also conduct research in many areas besides digital influence. If you’d like a quick recap of my earlier work, this video interview by MyCustomer.com covers the essence of what I did.
Most people seem to reference the 2007 report by Watts and Dodds (W&D) as scientific proof that influencers either don’t exist or don’t matter. I believe this academic report has been popularized in the industry by a 2008 Fast Company article: Is the Tipping Point Toast?. However, to truly understand the results by W&D, I recommend reading the original publication, which can be downloaded here for now.
If you managed to plow through the technical details, you will find that there is nothing wrong with W&D’s result. Their claims were simply misunderstood by people outside academia. In many cases, their claims were greatly exaggerated, and certainly over generalized beyond their valid regime. Let’s find out why...