虚假评论的识别及其对中国电子商务市场的影响

评论信息在我国电子商务市场已成为消费者决策的重要依据。因此商家为了利益有动机为自己制造虚假好评，为竞争对手制造虚假差评，从而危害市场经济秩序。然而针对我国虚假评论市场的研究仍较为缺乏。对虚假评论的成本分析显示，中国电子商务市场中虚假评论的成本结构区别于以Amazon为主的欧美电商市场，具体表现为购买虚假评论的成本处于较低的水平，成本结构的不同必然导致市场主体的决策不同。本文以中国某电商平台上关于宠物食品的评论数据为切入口，用数据驱动的方法识别并研究虚假评论对中国电子商务市场中的消费者和卖家造成的影响。

本文通过挖掘评论者行为和评论内容的异常，对部分虚假评论进行标记，将传统的识别问题转化为正样本无标签学习(Positive and Unlabeled Learning)问题。此外，还基于Bagging算法和XGBoost模型构建了一套自动识别虚假评论的系统。识别结果表明市场上存在大量虚假好评和相对较少的虚假差评。对识别结果进行实证分析，发现虚假好评通常是小规模店铺解决冷启动问题的营销手段，虚假差评是中大型店铺攻击竞争对手的策略。在虚假评论对销量的影响上看，短期内虚假好评确实能给商品销量带来一定的积极作用，虚假差评则会给销量带来程度更深的损害。但从长期来看，虚假好评反而会给店铺销量带来负面的影响。

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