Generative AI has ushered in the ability to generate content that closely
mimics human contributions, introducing an unprecedented threat: Deployed en
masse, these models can be used to manipulate public opinion and distort
perceptions, resulting in a decline in trust towards digital platforms. This
study contributes to marketing literature and practice in three ways. First, it
demonstrates the proficiency of AI in fabricating disinformative user-generated
content (UGC) that mimics the form of authentic content. Second, it quantifies
the disruptive impact of such UGC on marketing research, highlighting the
susceptibility of analytics frameworks to even minimal levels of
disinformation. Third, it proposes and evaluates advanced detection frameworks,
revealing that standard techniques are insufficient for filtering out
AI-generated disinformation. We advocate for a comprehensive approach to
safeguarding marketing research that integrates advanced algorithmic solutions,
enhanced human oversight, and a reevaluation of regulatory and ethical
frameworks. Our study seeks to serve as a catalyst, providing a foundation for
future research and policy-making aimed at navigating the intricate challenges
at the nexus of technology, ethics, and marketing.

AI 引发了对公众意见的操纵和扭曲，本研究以三个方面为基础，展示了 AI 制造虚假 UGC 的能力，量化了 UGC 对市场研究的干扰影响，并提出和评估了高级检测框架，呼吁综合手段保护市场研究的安全，包括高级算法解决方案、人工审核和伦理规范，为未来研究和制定政策提供基础。

保护市场研究：AI 制造的虚假信息的产生、识别和缓解

Safeguarding Marketing Research: The Generation, Identification, and  Mitigation of AI-Fabricated Disinformation

The age of social media has opened new opportunities for businesses. This
flourishing wealth of information is outside traditional channels and
frameworks of classical marketing research, including that of Marketing Mix
Modeling (MMM). Textual data, in particular, poses many challenges that data
analysis practitioners must tackle. Social media constitute massive,
heterogeneous, and noisy document sources. Industrial data acquisition
processes include some amount of ETL. However, the variability of noise in the
data and the heterogeneity induced by different sources create the need for
ad-hoc tools. Put otherwise, customer insight extraction in fully unsupervised,
noisy contexts is an arduous task. This research addresses the challenge of
fully unsupervised topic extraction in noisy, Big Data contexts. We present
three approaches we built on the Variational Autoencoder framework: the
Embedded Dirichlet Process, the Embedded Hierarchical Dirichlet Process, and
the time-aware Dynamic Embedded Dirichlet Process. These nonparametric
approaches concerning topics present the particularity of determining word
embeddings and topic embeddings. These embeddings do not require transfer
learning, but knowledge transfer remains possible. We test these approaches on
benchmark and automotive industry-related datasets from a real-world use case.
We show that our models achieve equal to better performance than
state-of-the-art methods and that the field of topic modeling would benefit
from improved evaluation metrics.

社交媒体时代为企业开启了新的机遇，但其中的文本数据分析则面临着诸多挑战。本研究针对嘈杂、大数据背景下的无监督主题提取问题，提出了三种基于变分自编码器框架的方法，并在实际使用案例中对这些方法进行了测试，证明了它们相较于现有方法在主题建模领域能够取得同等或更好的性能，并指出主题建模领域需要改进评估指标。