In today's digital economy, virtually everything from products and services to political debates and cultural phenomena can spark WOM on social media. Analyzing online WOM poses at least three challenges. First, online WOM typically consists of unstructured data that can transform into myriad variables, necessitating effective dimension reduction. Second, online WOM is often continuous and dynamic, with the potential for rapid, time-varying changes. Third, significant events may trigger symmetric or asymmetric responses across various entities, resulting in “bursty” and intense WOM from multiple sources. To address these challenges, we introduce a new computationally efficient method—multi-view sequential canonical covariance analysis. This method is designed to solve the myriad online WOM conversational dimensions, detect online WOM dynamic trends, and examine the shared online WOM across different entities. This approach not only enhances the capability to swiftly interpret and respond to online WOM data but also shows potential to significantly improve decision-making processes across various contexts. We illustrate the method's benefits through two empirical examples, demonstrating its potential to provide profound insights into online WOM dynamics and its extensive applicability in both academic research and practical scenarios.

中文翻译：

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分析在线口碑动态：一种新颖的方法


在当今的数字经济中，从产品和服务到政治辩论和文化现象，几乎所有事物都可以在社交媒体上引发口碑传播。分析在线口碑至少面临三个挑战。首先，在线口碑通常由非结构化数据组成，这些数据可以转化为无数变量，需要有效的降维。其次，在线口碑通常是连续的、动态的，具有快速、随时间变化的潜力。第三，重大事件可能会触发各个实体的对称或不对称反应，从而导致来自多个来源的“突发”和强烈的口碑传播。为了解决这些挑战，我们引入了一种新的计算高效的方法——多视图顺序典型协方差分析。该方法旨在解决无数的在线口碑对话维度，检测在线口碑动态趋势，并检查不同实体之间共享的在线口碑。这种方法不仅增强了快速解释和响应在线口碑数据的能力，而且还显示出显着改善各种环境下的决策过程的潜力。我们通过两个实证例子说明了该方法的好处，展示了其为在线口碑动态提供深刻见解的潜力及其在学术研究和实际场景中的广泛适用性。