Dyadic: A Scalable Platform for Human-Human and Human-AI Conversation Research

Conversation is ubiquitous in social life, but the empirical study of this interactive process has been thwarted by tools that are insufficiently modular and unadaptive to researcher needs. To relieve many constraints in conversation research, the current tutorial presents an overview and introduction to a new tool, Dyadic (https://www.chatdyadic.com/), a web-based platform for studying human-human and human-AI conversations using text-based or voice-based chats. Dyadic is distinct from other platforms by offering studies with multiple modalities, AI suggestions (e.g., in human-human studies, AI can suggest responses to a participant), live monitoring (e.g., researchers can evaluate, in real time, chats between communicators), and survey deployment (e.g., Likert-type scales, feeling thermometers, and open-ended text boxes can be sent to humans for in situ evaluations of the interaction), among other consequential features. No coding is required to operate Dyadic directly, and integrations with existing survey platforms are offered.

Journalists, Emotions, and the Introduction of Generative AI Chatbots: A Large-Scale Analysis of Tweets Before and After the Launch of ChatGPT

As part of a broader look at the impact of generative AI, this study investigated the emotional responses of journalists to the release of ChatGPT at the time of its launch. By analyzing nearly 1 million Tweets from journalists at major U.S. news outlets, we tracked changes in emotional tone and sentiment before and after the introduction of ChatGPT in November 2022. Using various computational and natural language processing techniques to measure emotional shifts in response to ChatGPT's release, we found an increase in positive emotion and a more favorable tone post-launch, suggesting initial optimism toward AI's potential. This research underscores the pivotal role of journalists as interpreters of technological innovation and disruption, highlighting how their emotional reactions may shape public narratives around emerging technologies. The study contributes to understanding the intersection of journalism, emotion, and AI, offering insights into the broader societal impact of generative AI tools.

Science Written by Generative AI is Perceived as Less Intelligent, but More Credible and Trustworthy than Science Written by Humans

This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance public trust in science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work assessed linguistic simplicity across such summaries and public perceptions. Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used GPT-4 to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Finally, Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable public perceptions of scientists (their credibility, trustworthiness) than more complexly-written human PNAS summaries. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society.