Influence – not as simple as Gladwell would have you believe!

The invited talks at ICWSM were especially good this year. I want to highlight a few points from Duncan Watt’s talk and Jon Kleinberg’s talks.

1. Social influence makes the selection for success less predictable. In other words, judged against independent measures of quality, if an audience is influenced by knowledge of community behaviour, it will select or promote with less correlation to quality than you would think. You may think ‘so much for the wisdom of the crowds’ but, of course, WOC is all about aggregating over independent judgments, not socially influenced ones – see Experimental Studies of Inequality and Unpredictability in an Artificial Cultural Market.
2. We know less about our friends than we think we do. In the Friend Sense experiment, it was demonstrated that we project our opinions onto friends about whom we make assumptions regarding political beliefs. Watt’s concerns about the misrepresentation of polarization might be contrasted with the experiments reported in Nick Carr’s book The Big Switch in which a) small preferences lead to deep segregation and b) homophilly leads to extremism.
3. Diffusion of information may ‘long circuit’ the small worlds of social networks. In Kleinberg’s presentation regarding the study of the largest internet chain mail (a petition) he described the role of the threshold model of diffusion in which we require multiple receipts of a stimulus (e.g. a chain mail letter) to pass it on, we are more sensitive to our immediate community – our strong links – than to small-world building weak links. This seems to have some relationship with Watt’s work on Challenging the Influentials Hypothesis and both his criticism of the disease analogy and his focus on the importance of the network structure, not some magical power of the ‘influential’.

Comments

[Antony Mayfield]

Brilliant concise post, sir - cheers. It's got my mind racing...

[Nick DiGiacomo]

Your point 1) reinforces something that most people in the social media world don't seem to appreciate/understand - statistical approaches that presume independence of events (frequentist and naive Bayes) can't be used reliably to analyze data sourced from social media/news. These data aren't crowdsourced in the WOC sense - they are "mobsourced". And the paper you cite reinforces the point that there is much valuable information contained in the correlations between mobsourced events. Capturing and interpreting these correlations - as opposed to throwing them out - is key to getting something new and valuable from social media/news.

[Daniel Tunkelang]

Matt Salganik's "artificial cultural market" work is brilliant. And I wonder how well consumers understand the ease of manipulating the information they receive. I'm not talking something as conspiracy theory-esque as Chomsky's Manufacturing Consent or Thomas Frank and Matt Weiland's Commodify Your Dissent, but rather more mundane hi-jinks like people writing shill reviews on Amazon.