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

arXiv: https://arxiv.org/abs/2403.14706 · HTML: https://mlsanity.com/paper/2403.14706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text](https://mlsanity.com/paper/2301.13852/) (2023)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)

## Abstract (arXiv)

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.