Key points are not available for this paper at this time.
Fear induction in the form of stories and visual images pervades the history of human culture. Creating a visceral emotion such as fear remains one of the cornerstones of human creativity. As artificial intelligence makes strides in solving challenging analytical problems like chess and Go, an important question still remains: can machines induce extreme human emotions, such as fear? In this work, we propose a deep-learning based collaborative horror writer that collaboratively writes scary stories with people on Twitter. We deploy our system 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-SF 43 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. Furthermore, we make three novel datasets used in our framework publicly available at https://github.com/catlab-team/shelley for encouraging further research on this topic.
Yanardag et al. (Fri,) studied this question.