人工智能在在线评论中的作用
The Role of AI in Online Reviews
- The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用LLM供给冲击和Trustpilot上1300万条评论，发现AI生成内容使未验证评论更负面，并引发集中爆发，表明生成式AI正重塑平台声誉与竞争动态。
AI中文摘要：
大型语言模型（LLM）的快速普及为在线平台上的策略性内容生成创造了新机会，包括可能有害的操纵形式，这些操纵可能削弱平台有效性并重塑平台动态。然而，衡量此类活动十分困难，因为人工智能生成的内容很少能被直接观察到。我们提出了一种实证方法，利用离散的LLM供给冲击——即模型价格和能力的突然变化，并将已验证评论与未验证评论进行对比，以识别与生成式AI供给改善相关的平台活动变化。我们将此方法应用于来自Trustpilot（领先的企业评论在线平台之一）的超过1300万条评论。一个稳健的发现是，在LLM供给冲击之后，未验证评论趋向于更强的负面性：1星评论增多，5星评论减少，评分降低，这些效应主要由新模型发布驱动，并集中在评论量最低和最高的企业中，表明策略性AI使用可能重塑平台竞争动态。我们进一步发现，LLM供给冲击会引发短暂而集中的评论活动爆发。综合来看，这些发现表明，生成式AI已经在重塑在线平台上声誉和竞争的运作方式。
英文摘要：
The rapid adoption of large language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially harmful forms of manipulation that may undermine platform effectiveness and reshape platform dynamics. However, measuring such activity is difficult because AI-generated content is rarely directly observable. We introduce an empirical approach that leverages discrete LLM supply shocks - abrupt changes in model prices and capabilities, and contrasts verified with non-verified reviews to identify changes in platform activity associated with generative AI supply improvements. We apply this approach to more than 13 million reviews from Trustpilot, one of the leading online platforms for business reviews. A robust finding is that following LLM supply shocks, unverified reviews shift toward greater negativity: more 1-stars, fewer 5-stars, and lower ratings, with effects driven primarily by new model releases and concentrated among firms with the lowest and highest review volumes, suggesting that strategic AI use may reshape platform competition dynamics. We further find that LLM supply shocks trigger short, concentrated bursts of review activity. Together, these findings suggest that generative AI is already reshaping how reputation and competition operate on online platforms.
