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Finding Deceptive Opinion Spam by Any Stretch of the Imagination

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Figure

Table 1: Descriptive statistics for 400 deceptive opinionspam submissions gathered using AMT
Table 2: Performance of three human judges and two meta-judges on a subset of 160 opinions, corresponding to thefirst fold of our cross-validation experiments in Section 5
Table 3: Automated classifier performance for three approaches based on nested 5-fold cross-validation experiments.Reported precision, recall and F-score are computed using a micro-average, i.e., from thepositive and false negative rates, as suggested by Fo
Table 4: Average feature weights learned by POSSVM. Based on work by Rayson et al. (2001), we expect weights onthe left to be positive (predictive of truthful opinions), and weights on the right to be negative (predictive of deceptiveopinions)

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