“Shelley: A Crowd-Sourced Collaborative Horror Writer”, Pinar Yanardag Delul, Manuel Cebrian, Iyad Rahwan2021-06-15 (fiction by AI, RNN, anxiety; similar)⁠:

In this work, we propose a deep-learning based collaborative horror writer [RNN] that collaboratively writes scary stories with people on Twitter. We deploy our system [on October 2017] as a bot on Twitter that regularly generates and posts new stories on Twitter, and invites users to participate. Users who interact with the stories produce multiple storylines originating from the same tweet, thereby creating a tree-based story structure.

We further perform a validation study on n = 105 subjects to verify whether the generated stories psychologically move people on psychometrically validated measures of effect and anxiety such as I-PANAS-SF⁴³ and STAI-SF.²⁶ Our experiments show that (1) stories generated by our bot as well as the stories generated collaboratively between our bot and Twitter users produced statistically-significant increases in negative affect and state anxiety compared to the control condition, and (2) collaborated stories are more successful in terms of increasing negative affect and state anxiety than the machine-generated ones. [This claim does not seem to be supported by their reported statistics in §4…]

Furthermore, we make 3 novel datasets used in our framework publicly available at GitHub for encouraging further research on this topic.

See Also:

• “Collaborative Storytelling with Large-scale Neural Language Models”

• “Artificial Intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry”

• “Scarecrow: A Framework for Scrutinizing Machine Text”

• “Introducing Aspects of Creativity in Automatic Poetry Generation”

• “Hierarchical Neural Story Generation”

• “Learning to summarize from human feedback”

• “Jigsaw: Large Language Models meet Program Synthesis”

• “Reframing Human-AI Collaboration for Generating Free-Text Explanations”

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