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

TL;DR
This paper explores how AI-generated disinformation impacts marketing research, demonstrating its ability to mimic authentic content, quantifying its disruptive effects, and proposing advanced detection methods to safeguard research integrity.
Contribution
It introduces new insights into AI fabricating disinformative content, quantifies its impact on marketing analytics, and evaluates advanced detection frameworks beyond standard techniques.
Findings
AI can generate disinformative content that mimics authentic UGC
Disinformation significantly disrupts marketing research analytics
Standard detection techniques are insufficient for filtering AI-generated disinformation
Abstract
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Big Data and Business Intelligence · Artificial Intelligence in Healthcare and Education
