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

Abstract

This manuscript introduces a novel method for conducting behavioral and social research by streaming real-time information to participants and manipulating content for experimental purposes via AI. We present an extension of the Samply software, which facilitates the integration of event-related data with mobile surveys and experiments. To assess the feasibility of this method, we conducted an experiment where news headlines were modified by a Chat-GPT algorithm and streamed to participants via the Samply Stream API and mobile push notifications. Feedback from participants indicated that most did not experience technical problems. There was no significant difference in readability across original, paraphrased, and misinformation-injected news conditions, with only 1.2% of all news items reported as unreadable. Participants reported significantly less familiarity with misinformation-injected news (84% unfamiliarity) compared to original and paraphrased news (73% unfamiliarity), suggesting successful manipulation of information without compromising readability. Dropout and non-response rates were comparable to those in other experience sampling studies. The streaming method offers significant potential for various applications, including public opinion research, healthcare, marketing, and environmental monitoring. By enabling the real-time collection of contextually relevant data, this method has the potential to enhance the external validity of behavioral research and provides a powerful tool for studying human behavior in naturalistic settings.