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International Conference on Human-Computer Interaction

HCII 2022: Human Interface and the Management of Information: Applications in Complex Technological Environments pp 65–79Cite as

Understanding User Perception and Trust when Results from a Dating Abuse Detection Application Are Displayed

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13306))

Abstract

SecondLook is a mobile phone application that uses machine learning algorithms to detect digital dating abuse from text messages. An online survey was conducted where participants (N = 202) were provided with three different visualizations of the detection screen (labeled text only, percentage of abusive text messages only, and labeled and percentage of abusive text messages) to understand a) What is the threshold of abusive text messages that would motivate the user to consider themselves in an abusive relationship? (30%, 50%, or 70% abusive text messages) b) What is the most effective way to visualize the results of the detection classifier that would invoke user trust and encourage them to receive necessary help? c) Which visualizations nudge users to trust machine learning-based classification results? We found the Text only condition to show significant differences across all three research questions and will use these results for future iterations of SecondLook.

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Acknowledgements

We would like to thank Dr. Jerome McClendon and Ms. Adriana Souza for their contributions to the research methodology and data analysis.

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Authors and Affiliations

  1. New College of Florida, Sarasota, FL, USA

    Tania Roy & Fehmi Neffati

  2. North Carolina School of Science and Mathematics, Morganton, NC, USA

    Larry F. Hodges

Authors
  1. Tania Roy
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  2. Larry F. Hodges
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  3. Fehmi Neffati
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Corresponding author

Correspondence to Tania Roy .

Editor information

Editors and Affiliations

  1. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  2. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

Appendix

Appendix

Table 3. Number of mislabeled text messages, per condition, presented to participants randomly assigned to 9 experimental conditions. In the table below column C9-C1 are experimental conditions. Column labelled Miss refers to total missed classifications or classification errors
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Roy, T., Hodges, L.F., Neffati, F. (2022). Understanding User Perception and Trust when Results from a Dating Abuse Detection Application Are Displayed. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information: Applications in Complex Technological Environments. HCII 2022. Lecture Notes in Computer Science, vol 13306. Springer, Cham. https://doi.org/10.1007/978-3-031-06509-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-06509-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06508-8

  • Online ISBN: 978-3-031-06509-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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