论文标题
迈向一个人口更大的在线平台：经济评论的经济影响
Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews
论文作者
论文摘要
如今，许多公司为在线评论（称为补偿评论）提供赔偿，预计将增加其非补偿评论的数量和整体评级。这个策略有效吗？在哪些主题或主题上，此策略效果最好？这些问题仍未在文献中得到回答，而是引起了行业的重大兴趣。在本文中，我们通过从运输网站上利用1,240家汽车运输公司的在线评论来研究补偿评论对非补偿评论的影响。由于某些在线评论缺少有关其薪酬状况的信息，因此我们首先开发了一种分类算法，以通过利用基于机器学习的识别过程来区分不补偿的评论，并利用补偿评论的独特功能。从分类结果中，我们从经验上研究了补偿评论对非补偿的影响。我们的结果表明，补偿评论的数量确实增加了非补偿评论的数量。此外，补偿评论的评分对非补偿评论的评分产生了积极影响。此外，如果经过补偿的评论以汽车运输功能的主题或主题为特征，则补偿评论对非补偿审查的积极影响是最强的。除了文本分类和经验模型中的方法论贡献外，我们的研究还提供了有关如何在改善平台的整体在线评论和评级方面证明如何证明经过补偿在线评论的有效性的经验证据。另外，它建议将其全部优势的补偿评论提供指南，也就是说，在这些评论中以某些主题或主题为特征以取得最佳结果。
Many companies nowadays offer compensation to online reviews (called compensated reviews), expecting to increase the volume of their non-compensated reviews and overall rating. Does this strategy work? On what subjects or topics does this strategy work the best? These questions have still not been answered in the literature but draw substantial interest from the industry. In this paper, we study the effect of compensated reviews on non-compensated reviews by utilizing online reviews on 1,240 auto shipping companies over a ten-year period from a transportation website. Because some online reviews have missing information on their compensation status, we first develop a classification algorithm to differentiate compensated reviews from non-compensated reviews by leveraging a machine learning-based identification process, drawing upon the unique features of the compensated reviews. From the classification results, we empirically investigate the effects of compensated reviews on non-compensated. Our results indicate that the number of compensated reviews does indeed increase the number of non-compensated reviews. In addition, the ratings of compensated reviews positively affect the ratings of non-compensated reviews. Moreover, if the compensated reviews feature the topic or subject of a car shipping function, the positive effect of compensated reviews on non-compensated ones is the strongest. Besides methodological contributions in text classification and empirical modeling, our study provides empirical evidence on how to prove the effectiveness of compensated online reviews in terms of improving the platform's overall online reviews and ratings. Also, it suggests a guideline for utilizing compensated reviews to their full strength, that is, with regard to featuring certain topics or subjects in these reviews to achieve the best outcome.
