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!
If you look back at the Webster’s definition of influence that I quoted earlier (see What is Influence, Really? – No Carrots, No Sticks, No Annoyance, No Tricks), it says: “The power or capacity of causing an effect in indirect or intangible ways : sway.” So, whatever the effects are (i.e. the changes in thought or behavior), it must be caused through “indirect or intangible ways.”
What does this mean? It is actually a subtle but important point that many discussions about influence have overlooked. It means that influence shouldn’t be something that people can affect directly and easily. This is the key to fixing the influence irony. Because people would otherwise be able to game the system and raise their own influence score without doing anything influential.
Why is the “Influence Irony” so Challenging?
To mitigate the gaming of influence, vendors must ensure that people can’t directly affect their influence. However, influence should be something that people can affect through their actions. Otherwise people’s influence will never change. So influence is something that people can affect, but not directly or easily.
This subtle point of influence is already very hard for people to grasp, but if you think deeply about it, it sort of makes sense. If you can directly change someone’s thoughts or behaviors, then you are no longer influencing them. You are controlling them, and that is not influence. Are you following me?
Now, the real challenge comes when you try to make this rigorous enough so you can write an algorithm to compute someone’s influence score. Part of the difficulty is because this statement is quite vague: “influence is something that people can affect, but not directly or easily.” How direct is considered indirectly, and how easy is not easily? What are the criteria to determine if something is too easy or too direct?
Even though we can understand this in words, it’s actually very hard to put it into equations and programming codes, and the precise criteria are very hard to determine. But luckily there is a solution! The solution is to leave it up to the crowd and let the population determine the criteria for you. This is the essence of an adaptive model.
What is an Adaptive Influence Model?
First let me clarify the difference between an adaptive model vs. an adaptive score:
How to Adapt your Influence Model to the Population?
The best way to understand how adaptive models work is to build one. So I will try to walk through the essence with you using an example that we’ve discussed earlier concerning follower count. When we applied the definition of influence, we came to the conclusion that followers shouldn’t affect someone’s influence score, because too many people will just follow back blindly as a quid pro quo to someone following them.
Rather than eliminating the follower metric from the model completely, adaptive models will still make use of this data, but the model will ensure that follower metric doesn’t affect the final influence score (e.g. by assigning a weight of zero to the follower metric). You may wonder why bother using the follower metric if we want to ensure that it has no effect? This is the essence of an adaptive model, because now we can use behavior feedback data from the population to adjust this weight.
If everyone (i.e. 100% of the population) is following back blindly, then follower count is merely a reflection of the quid pro quo behavior, which is not influence. In this case the weight should be set to 0. On the other hand, if one day people become very selective of whom they follow and only follow people who produces good content that affects their everyday decision, then follower count should contribute to someone’s influence score. In this case, the weight should be set close to 1. In an adaptive model, we dynamically adjust the model (e.g. the weight for follower count, or the specific formula and algorithm that we use the compute influence scores) based on the collective behavior of the population.
If you’ve tried to develop an influence scoring algorithm, you have probably analyzed a lot of data and convinced yourself that reciprocity metrics such as retweets or likes are good metrics for estimating someone’s influence score. What happens if people’s behavior change tomorrow?
If everyone starts to retweet everything they received and liking everything they see because they get points and badges for doing that (via gamification), would retweets and likes still be good metrics to use in your influence model? They may still be important, but they should probably be weighted down significantly. Therefore, 2 extra steps are needed to make an ordinary influence model adaptive:
Why are Adaptive Models so Hard to Build?
Adaptive models are very hard to construct, because every metric that is used by the algorithm can be gamed. Furthermore, there are probably many ways to game a particular metric. Thus, for every metric the algorithm uses, there are probably 10 or 20 auxiliary metrics the system needs to monitor in order to determine whether people are indeed gaming a metric.
Regardless of the challenges, this is exactly what search engines are doing to prevent SEO experts from gaming their relevance ranking algorithm. As soon as a significant amount of people have figured out a scheme to game a metric used in search ranking, the algorithm will quickly adjust itself to reduce the impact of the gamed metric on the final ranking. Since search engines make use of hundreds of metrics for their relevance ranking, they need to track tens of thousands of auxiliary metrics in order to adapt their algorithm to the population. To make things worse, popular search engines (e.g. Google, Bing, etc.) are also hot targets for this type of gaming (i.e. SEO) behavior, so their ranking algorithms are literally changing every day. After almost 15 years, Google is still adding metrics to their system to improve its ability to adapt to the searching and browsing behavior of its users.
Most influence scoring algorithms uses fewer metrics (i.e. less than 50 metrics) than search engines, so the number of auxiliary metrics these algorithms need to monitor is also fewer. However, as we discussed previously, influence scoring algorithms are not only more susceptible to gaming, they are also easier to game. They need to adapt faster in order to counteract the negative effect of the influence irony.
So we have good news and bad news. The bad news is that the influence irony is an inevitable consequence of scoring people’s influence. But the good news is that there is a way to fix this problem through adaptive models. Although building an adaptive model is challenging, it can be done. The search engine industry has demonstrated its feasibility at the planetary scale. The fact that search engines are still working pretty well is an indication that their relevance ranking algorithms is able to adapt to the gaming behavior of the SEO industry. Otherwise, search engines would return the most SEO optimized page with respect to a query rather than the most authority and relevant pages. And search quality would significantly degrade as SEO continue to game their ranking algorithm.
Currently, no influence vendor uses adaptive algorithm to score influence. But influence is a very nascent industry, and there is a lot for them to learn from the more mature search engine industry. The outlook of the influence industry may be very bright (i.e. where influence scores truly reflect people’s capacity to influence) or very gloomy (i.e. where influence scores only reflects how effectively people game the scoring algorithm). Which future becomes reality is going to depend on how quickly they can implement an adaptive model of influence.
Alright, next time I am going to roll the dice and see what topics I end up writing. Or you can tell me what topics you like to hear. In the meantime, I welcome any comments, discussion, kudos, critiques, and/or challenges. See you next time.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
You must be a registered user to add a comment here. If you've already registered, please log in. If you haven't registered yet, please register and log in.