Randomized algorithms
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Wisdom of Crowds
How Bad is Forming Your Own Opinion?
FOCS '11 Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
CrowdScreen: algorithms for filtering data with humans
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Deco: a system for declarative crowdsourcing
Proceedings of the VLDB Endowment
Characterizing and curating conversation threads: expansion, focus, volume, re-entry
Proceedings of the sixth ACM international conference on Web search and data mining
Coevolutionary opinion formation games
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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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.