The EGU interactive community platform
The EGU interactive community platform
EGUsphere
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Preprints
https://doi.org/10.5194/egusphere-2026-5876
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-5876
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Preprints
22 Oct 2026
 | 22 Oct 2026
Status: this preprint is open for discussion and under review for Geoscience Communication (GC).

We <3 Skynet: Or, How We Learned to Stop Worrying and Love the Bot Responses in Our Survey Data

Lynda Olman, Benjamin J. Hatchett, Meghan Collins, Anne Heggli, Kathryn Lambrecht, Zach Tolby, and Dominic Charles

Abstract. A case study in methods reveals a way that false responses to online surveys can be leveraged to add rhetorical value to technical communication research. Serendipitously, we discovered during our study of community commonplaces of weather-forecast discussions that open-ended bot responses contained phrases very close to rhetorical commonplaces generated by humans. Using freely available text-mining tools, technical communication researchers can easily mine false responses for indicators of community beliefs, norms, and values. This LESSONS Report (Limitations, Errors, Surprises, Shortcomings, Opportunities for New Science) exemplifies a surprising situation that could introduce limitations or errors (if unaddressed) but instead is converted to an opportunity to increase the robustness of survey data.

How to cite. Olman, L., Hatchett, B. J., Collins, M., Heggli, A., Lambrecht, K., Tolby, Z., and Charles, D.: We <3 Skynet: Or, How We Learned to Stop Worrying and Love the Bot Responses in Our Survey Data, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-5876, 2026.
Received: 26 Sep 2026 – Discussion started: 22 Oct 2026
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Lynda Olman, Benjamin J. Hatchett, Meghan Collins, Anne Heggli, Kathryn Lambrecht, Zach Tolby, and Dominic Charles

Status: open (until 17 Dec 2026)

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Lynda Olman, Benjamin J. Hatchett, Meghan Collins, Anne Heggli, Kathryn Lambrecht, Zach Tolby, and Dominic Charles
Lynda Olman, Benjamin J. Hatchett, Meghan Collins, Anne Heggli, Kathryn Lambrecht, Zach Tolby, and Dominic Charles
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Latest update: 22 Oct 2026
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Short summary
Online surveys are valuable data sources supporting social science research. However, they are increasingly susceptible to false responses from both bots that may be difficult to discern from humans. Using free tools, we report a simple method to leverage these false responses to extract indicators of weather-related community beliefs, norms, and values. This situation could introduce errors (if unaddressed) but instead we use it as an opportunity to increase the robustness of survey data.
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