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A linguistic comparison between human- and AI-generated content.

Flávia A Rodrigues1, Niclas F Sturm1, Flávio L Pinheiro1

  • 1NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisboa, Portugal.

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Summary
This summary is machine-generated.

This study analyzed Portuguese texts, finding AI-generated content is formal and positive, unlike varied human writing. Misinformation detection tools struggle with AI outputs, highlighting the need for improved methods against AI-driven misleading content.

Keywords:
artificial intelligencelinguisticssocial sciences

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Distinguishing AI-generated text from human writing is crucial for information integrity.
  • Large Language Models (LLMs) produce increasingly sophisticated text, posing challenges for detection.
  • Linguistic analysis offers potential methods for identifying AI-generated content, especially in specific languages like Portuguese.

Purpose of the Study:

  • To investigate the distinct linguistic features of AI-generated texts compared to human-written texts in Portuguese.
  • To evaluate the effectiveness of current misinformation detection models on AI-generated content.
  • To identify characteristics that differentiate AI-generated misinformation from human-generated misinformation.

Main Methods:

  • Creation of two datasets: human-written (factual and false) and AI-generated texts using advanced LLMs (GPT-4o, Mistral Large, Llama 3.3 70B).
  • Application of linguistic analysis tools, including Linguistic Inquiry and Word Count (LIWC) and Sparse Additive Generative Model (SAGE).
  • Assessment of a misinformation detection model's performance on both human and AI-generated text datasets.

Main Results:

  • AI-generated texts exhibit more formal, structured, positive, and motivational linguistic traits.
  • Human-written texts show greater variability in length, include negative emotions, and personal references.
  • The misinformation detection model achieved 93% accuracy on human texts but only 75% on LLM-generated texts.

Conclusions:

  • AI-generated content possesses unique linguistic patterns that differ significantly from human writing.
  • Current misinformation detection models face limitations in accurately identifying AI-generated misleading content.
  • Enhanced detection strategies are necessary to effectively combat AI-driven misinformation in Portuguese and other languages.