消除社会智慧的偏见

Abhimanyu Das, Sreenivas Gollapudi, R. Panigrahy, Mahyar Salek
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引用次数: 47

摘要

随着社会网络的爆炸性增长,许多应用程序越来越多地利用在线人群的脉搏来完成各种任务,如营销、广告和意见挖掘。一个重要的例子是群体效应的智慧,这已经被很好地研究了当人群没有相互作用时的任务。然而,这些研究并没有明确指出社交网络中的网络效应。在这种情况下,一个关键的区别是,这些互动产生的社会影响可能会破坏群体的智慧。使用自然的意见形成模型,我们分析了这些互动对个人意见的影响,并估计了她的顺从倾向。然后,我们提出了有效的抽样算法,结合这些一致性值,以达到对人群智慧的无偏见估计。我们分析了样本大小和估计误差之间的权衡,并使用从在线用户实验和合成数据中获得的真实数据验证了我们的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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