Can We Learn From Anti-Social Users?

November 21, 2009
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One of the interesting challenges we face as both developers and consumers of search technology is that social signals are a double-edged sword. On one hand, social signals have proven essential in distinguishing signal from noise – be they links, re-tweets, or any number other ways that online consumers (or more correctly “prosumers”) actively and passively communicate value judgments about information. On the other hand, our reliance on these social signals makes us vulnerable to positive feedback and spammers.

Consider MusicLab, an “experimental study of self-fulfilling prophecies in an artificial cultural market.” In this study, sociologists Matt Salganik, Peter Dodds, and Duncan Watts manipulated the social information available to consumers (specifically teens) regarding their peers’ musical tastes. The experimenters’ goal was to empirically validate a quantitative model of social contagion.

But we can look at this study another way: by isolating the social factors that influence musical taste, the experimenters were also isolating the non-social signal–in theory, how popular a song would be in the absence of social signaling. Indeed, they found that, if they measured a ..



One of the interesting challenges we face as both developers and consumers of search technology is that social signals are a double-edged sword. On one hand, social signals have proven essential in distinguishing signal from noise – be they links, re-tweets, or any number other ways that online consumers (or more correctly “prosumers”) actively and passively communicate value judgments about information. On the other hand, our reliance on these social signals makes us vulnerable to positive feedback and spammers.

Consider MusicLab, an “experimental study of self-fulfilling prophecies in an artificial cultural market.” In this study, sociologists Matt Salganik, Peter Dodds, and Duncan Watts manipulated the social information available to consumers (specifically teens) regarding their peers’ musical tastes. The experimenters’ goal was to empirically validate a quantitative model of social contagion.

But we can look at this study another way: by isolating the social factors that influence musical taste, the experimenters were also isolating the non-social signal–in theory, how popular a song would be in the absence of social signaling. Indeed, they found that, if they measured a song’s quality by isolating out the social factor, “the best songs never do very badly, and the worst songs never do extremely well, but almost any other result is possible”.

It’s interesting – interesting to me, at least! – to ask if search engines can do the same for search. One of the frequent objections to link-based authority measures like PageRank is that they make the rich get richer. “Real-time” variants like re-tweet frequency (and even TunkRank) suffer from the same weakness. Unchecked, these measures can cause authority / influence market has to resemble a winner-take-all market.

It strikes me as interesting to learn from cases where searchers swim upstream against the social signals to find information. Of course, you may already see the contradiction – this is just another kind of social signaling! Still, it seems like it might be a way to hedge our bets and against the weaknesses of positive feedback and spammers. In a similar vein, we might look at how users find information that suffers from poor accessibility or retrievability.

I don’t have answers about how to pursue such an approach, or whether it would even be feasible to do so. But I hope you agree with me that it’s an interesting question.

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