I’ve been writing on big data/analytics for the past 2 months, and I get a sense that people got a little data overloaded. So let me come back to the topic of influence and pick up where I left off.
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.
However, if you really want to understand the current state of influence scoring industry, you should still check out the following articles:
An Old Story of Search Engine Optimization
As the world wide web (www) grew in the early 90s and created a big data problem, human-maintained web directories were no longer a scalable solution for information retrieval from the internet. Powerful search engines (e.g. Lycos, AltaVista, Excite, Yahoo, Inktomi, Google, etc.) were developed to index the www in order to provide both scalable and efficient information retrieval (See Searching and Filtering Big Data - The 2 Sides of the “Relevance” Coin). In order to present the retrieved information to the user in a more meaningful way, search engines needed to rank their search results in terms of relevance and show the most relevant pages first.
Google developed an innovative relevance ranking algorithm (a.k.a. PageRank) based on the hyperlink structure of the www. The PageRank algorithm basically takes inputs (i.e. the hyperlink structures of the entire www) and cranks out a score for every webpage that, in theory, represents its authority on the www.
As we learn from the behavior economics of humans, when we put a score on something, we created an incentive for some people to want to improve their score. This is human nature. People care about themselves, they care about any comparisons that concern them, whether it is their webpages, car, home, their work, or just themselves. Some would go so far as to cheat the algorithm just to get a better score. In fact, Google’s PageRank has created an entire industry around gaming their score, and it’s called SEO. Although any SEO specialist may deny the fact that they are gaming the PageRank algorithm, they are constantly finding ways to artificially increase the PageRank of your webpages. Is this cheating? Some SEO schemes may be acceptable by Google, but there are definitely some that are considered cheating (e.g link farm and spamdexing).
The New Story of Influence Engine Optimization
Today, the social web has grown and gained massive adoption. And again, influence vendors are putting a number on something (i.e. people’s influence). Moreover, they made this score very public and visible. So people will again find ways to artificially increase their influence score. But there are three aspects that are different this time.
IEO is an inevitable consequence of scoring people’s influence. So, do influence scores still have any meaning? It’s definitely not a measure of someone’s influence; and it’s probably not even a measure of his potential influence anymore due to IEO.
So an influence score is really just a measure of how well people game the influence scoring algorithm.
If you responded a lot to your twitter stream yesterday and your influence score jump up today, you’ve just discovered that you can increase your influence score by responding more. Knowing this, would you continue to respond more? Most people probably would, especially if they care about their score. This has created a lot of noisy chatters who are not actually influential in any meaningful way. Their influence score is merely a reflection of the fact that they have successfully gamed the algorithm into giving them a higher score simply by responding more, but not actually doing anything truly influential.
Here is the irony: Because behaviors that game the system are typically a lot easier and simpler (see Simplicity Counts - Even in Gamification) than behaviors that are truly influential, IEO will tend to changes people’s behavior in a way that pushes them further away from being truly influential. Ironic isn’t it? That’s why I call this “influence irony.”
SEO is a natural consequence of the fact that search engines (e.g. Google) is attaching a score (i.e. PageRank) to the webpages on the www. Today history is repeating itself as influence vendors are assigning an influence score to social media participants. Consequently, people will inadvertently change their behavior to game the influence scoring algorithms in order to optimize their score. Although influence engine optimization (IEO) is inevitable, and in many ways similar to SEO, three aspects of IEO differ from SEO:
What does this means? It means influence scores will become less and less accurate as a measure of someone’s potential influence, and more of a reflection of how successful someone has gamed the influence scoring algorithm.
It is quite a pity that the influence vendors not only failed to quantify people’s potential influence, but also created lots of loud and noisy wannabes who really haven’t done anything influential. But don’t be disappointed. Next time, let’s talk about how we can fix this. And there is a solution out there! If you’ve gotten this far with my writing on influence, don’t miss the next post! So stay tuned or follow me on twitter/Google+.
By the way, a good synopsis of some of my recent writing on influenced has been published on TechCrunch as well as WIRED UK. So if you like to review the main points of my previous posts without getting all the gory detail, you should check them out. Meanwhile, let’s open the floor to further discussions.
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+.
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