Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models
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Abstract (arXiv)
This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 0 of its 17 references that are in the map. First posted on arXiv 6 Mar 2024.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2026Reasoning Models Struggle to Control their Chains of Thoughtcited by 22
- 2025Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Modelscited by 14
- 2025ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputscited by 6
- 2026JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attackscited by 6
- 2026Measuring Pragmatic Influence in Large Language Model Instructionscited by 3
- 2026Reasoning Models Struggle to Control their Chains of Thought5 Mar 2026
- 2026JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks1 Mar 2026
- 2026Measuring Pragmatic Influence in Large Language Model Instructions2 Feb 2026
- 2025Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Models24 Oct 2025
- 2025ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputs17 Jun 2025