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Samply Stream API: The AI-enhanced method for real-time event data streaming.

Yury Shevchenko1, Ulf-Dietrich Reips2

  • 1Research Methods, Assessment, and iScience; Department of Psychology, University of Konstanz, Universitätsstraße 10, Box 31, 78464, Konstanz, Germany. yury.shevchenko@uni-konstanz.de.

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Summary
This summary is machine-generated.

This study introduces AI-driven real-time content streaming for behavioral research. The novel method successfully manipulated news headlines, demonstrating feasibility for future studies in public opinion and healthcare.

Keywords:
AI-enhanced methodExperience sampling methodMobile surveysReal-time data streamingSamply Stream API

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Area of Science:

  • Behavioral and social sciences
  • Human-computer interaction
  • Computational social science

Background:

  • Traditional behavioral research methods often lack real-time data collection capabilities.
  • Integrating event-related data with mobile surveys and experiments presents technical challenges.
  • AI offers potential for dynamic content manipulation in experimental designs.

Purpose of the Study:

  • To introduce and assess a novel method for real-time behavioral and social research using AI-driven content streaming.
  • To evaluate the feasibility of manipulating content, specifically news headlines, via AI for experimental purposes.
  • To determine the impact of AI-manipulated content on participant experience, readability, and information familiarity.

Main Methods:

  • An extension of the Samply software was developed to integrate event-related data with mobile surveys and experiments.
  • An experiment was conducted where news headlines were modified by a Chat-GPT algorithm and streamed to participants via the Samply Stream API and mobile push notifications.
  • Participant feedback, readability assessments, and familiarity with news conditions were collected and analyzed.

Main Results:

  • The streaming method was feasible, with most participants reporting no technical issues.
  • No significant difference in readability was found across original, paraphrased, and misinformation-injected news conditions.
  • Participants showed significantly less familiarity with misinformation-injected news, indicating successful manipulation without compromising readability.
  • Dropout and non-response rates were comparable to existing experience sampling studies.

Conclusions:

  • The AI-driven real-time content streaming method is a feasible and effective tool for behavioral and social research.
  • This approach enhances the external validity of research by enabling data collection in naturalistic settings.
  • Potential applications span public opinion research, healthcare, marketing, and environmental monitoring, offering a powerful tool for studying human behavior.