If Politics and Depression, Why Not Motivation?
Data mining has many applications. Every data mining freshman must have heard the cool story of how Wal-Mart used association rules for increasing their selling rates. This story, which according to many is only an urban legend, demonstrates what we all now know: Data mining is good for e-commerce.
Take, for example, Amazon.com. In Web Mining background presentations I give to M.A. students in courses discussing (research of) Web-based Learning Environments, I usually demonstrate to them the “surfing experience” in this huge Website. First, I login with my real username/password combination, and go over the list of recommended items. Here is a glance to the first three items in it (click thumbnail to enlarge):
The list is built mostly of history novels, history of science non-fictions, and some networking gadgets. Then, after a few keystrokes during which I change the username/password, I reveal the Mr. Hyde in me, and although the system is still greeting me with my real name, a totally different list of recommendations appears (click thumbnail to enlarge):
Suddenly, Origami and napkin folding (that’s pretty cool!) populate the wish-list I presumably wish to list. Since both accounts were used by the same (real) person and from the same machine (i.e., same IP), it’s clear enough that Amazon has been using the usage data (i.e., log files) of its customers and surfers in order to “help customers find what they want and purchase it in a way that is simple and convenient” (i.e., increase the store’s selling numbers; quote by Amazon’s Senior Manager for Data Mining, taken from here).
Well, although it might be funny (or not), this little example demonstrates the well-known power of data mining. However, what I’m most curious about are the less-known-sometimes-strange potential implications of it.
Take, for example, the future! Moreover, think about how depressing it will be. Now, when you’re already bad-mooded, it’s the right time to cheer you up with some great news: Data mining has shown that although medium-term future will be quite confusing, long-term future will be good! This fascinating result was presented by Alberto Pepe of UCLA (paper was co-authored with Johan Bollen LANL) in AAAI 2008 Spring Symposium on Emotion, Personality, and Social Behavior (held three weeks ago at Stanford University, Palo Alto, CA). The full paper can be reached via arXiv.org.
Well, thinking about it, data mining has to do with forecasting, so even depression forecasting doesn’t seem too weird. But, what about politics? Well, as been reported a few weeks ago, there is a strong connection between those two, and it is basically data mining application which brought NY Governor Eliot Spitzer to resign. The first link in the chain which finally brought to the 1000$-an-hour-prostitution-affair revelation was, as it turns out, a warning produced by a fraud-detecting software which uses data mining techniques to point out irregular money transactions.
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So, now I’m really encouraged. When I first told friends and peers that I’d like to use data mining methods for extracting motivation of online learners (and affective aspects of online learning in general), they looked at me in an odd way. “How do you think you might evaluate one’s motivation without seeing him, without hearing him, without talking to him?”
Well, now I’m sure I’m not the only dreamer. If politics and depression may benefit from data mining, why shouldn’t affective learning research? It seems that my feelings regarding it had been phrased already:
You may say I’m a dreamer, but I’m not the only one
(Imagine, John Lennon)
Well, this is a great excuse to meet again with this wonderful song…