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Abstract

Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication.

DOI

10.30658/hmc.6.5

Author ORCID Identifier

Keri K. Stephens: 0000-0002-9526-2331

Anastazja G. Harris: 0000-0003-3756-1453

Amanda Hughes: 0000-0002-7506-3343

Carolyn E. Montagnolo: 0000-0001-9067-943X

Karim Nader: 0000-0003-3571-1796

Tara Tasuji: 0000-0003-1054-5218

Yifan Xu: 0000-0001-5239-4951

Hemant Purohit: 0000-0002-4573-8450

Christopher W. Zobel: 0000-0002-0952-7322

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