The Anatomy of Online Reviews: Evidence from the Steam Store

在线评论现在是现代消费者信息集合中无处不在的一部分。但是对于消费者来说，评论作为产品质量的信号有多好呢？使用来自流行的“Steam”游戏平台的数据集，我研究了评论生成中的两个相关问题：评论者的自我选择和基于预先存在的消费者评估的质量判断的“启动”。2019年Steam上的一项政策改革既降低了评论的交易成本，也改变了预先存在的消费者质量评级的平均曝光水平，这在游戏和评论者的生命周期中是随机发生的。我发现，受政策变化影响而进行评论的新人对游戏的正面评价的可能性要高出4%，留下的评论要短20%，而且他们在游戏内部和跨游戏体验都比较缺乏经验。此外，减少评论者对产品平均评级的曝光会导致他们的评论对这种评级的依赖程度的实质性下降。这种效应是不对称的，评级较差的游戏会得到更多正面评价，这表明负面评价可能会导致评级下降。总的来说，这一政策能够更好地反映出游戏在玩家中的受欢迎程度，但是这些新评论者却被其他评论者认为不太有帮助，这意味着更准确的评论分数是以不太有帮助的评论为代价的。

Online reviews are now a ubiquitous part of the modern consumer's information set. But how good are reviews as signals of product quality for consumers? Using a dataset derived from the popular `Steam' gaming platform I investigate two linked issues in the generation of reviews: the self-selection of reviewers and the `priming' of quality judgements as based on pre-existing consumer assessments. A policy reform on Steam in 2019 both lowered the transaction cost of reviewing and changed the average level of exposure to pre-existing consumer quality ratings, and this randomly occured within a game and reviewer's life cycle. I find that the new individuals elicited to review by the policy change are 4% more likely to rate any game positively, leave 20% shorter reviews and are less experienced both within and across games. In addition, reducing the exposure of a reviewer to a product's average rating leads to a substantive drop in the dependency of their review on such a rating. This effect is asymmetric, with worse-rated games receiving more positive reviews, suggesting that negative opinions can compound to bias ratings downward. Overall, the policy led to a game's rating better reflecting its popularity with players, but these new reviewers were rated as less helpful by their peers, implying that more accurate review scores come at the cost of less helpful reviews.