Debiasing social wisdom

With the explosive growth of social networks, many applications are increasingly harnessing the pulse of online crowds for a variety of tasks such as marketing, advertising, and opinion mining. An important example is the wisdom of crowd effect that has been well studied for such tasks when the crowd is non-interacting. However, these studies don't explicitly address the network effects in social networks. A key difference in this setting is the presence of social influences that arise from these interactions and can undermine the wisdom of the crowd [17]. Using a natural model of opinion formation, we analyze the effect of these interactions on an individual's opinion and estimate her propensity to conform. We then propose efficient sampling algorithms incorporating these conformity values to arrive at a debiased estimate of the wisdom of a crowd. We analyze the trade-off between the sample size and estimation error and validate our algorithms using both real data obtained from online user experiments and synthetic data.