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Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias

机译:众包真实性:判断量表和评估者偏见的影响

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News content can sometimes be misleading and influence users' decision making processes (e.g., voting decisions). Quantitatively assessing the truthfulness of content becomes key, but it is often challenging and thus done by experts. In this work we look at how experts and non-expert assess truthfulness of content by focusing on the effect of the adopted judgment scale and of assessors' own bias on the judgments they perform. Our results indicate a clear effect of the assessors' political background on their judgments where they tend to trust content which is aligned to their own belief, even if experts have marked it as false. Crowd assessors also seem to have a preference towards coarse-grained scales, as they tend to use a few extreme values rather than the full breadth of fine-grained scales.
机译:新闻内容有时可能会产生误导,并影响用户的决策过程(例如,投票决策)。定量评估内容的真实性成为关键,但是这通常具有挑战性,因此需要专家来完成。在这项工作中,我们着眼于专家和非专家如何通过重点关注采用的判断量表的影响以及评估者对他们执行的判断的偏见来评估内容的真实性。我们的结果表明,评估者的政治背景对他们的判断有明显影响,他们倾向于信任符合自己信念的内容,即使专家将其标记为错误。人群评估者似乎也偏爱粗粒度标度,因为他们倾向于使用一些极值,而不是细粒度标度的全部宽度。

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