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Toward Interfaces that Help Users Identify Misinformation Online: Using fNIRS to Measure Suspicion

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Abstract

With terms like ‘fake news’ and ‘cyber attack’ dominating the news, skepticism toward the media and other online individuals has become a major facet of modern life. This paper views the way we process information during HCI through the lens of suspicion, a mentally taxing state that people enter before making a judgment about whether or not to trust information. With the goal of enabling objective, real-time measurements of suspicion during HCI, we describe an experiment where fNIRS was used to identify the neural correlates of suspicion in the brain. We developed a convolutional long short-term memory classifier that predicts suspicion using a leave-one-participant-out cross-validation scheme, with average accuracy greater than 76%. Notably, the brain regions implicated by our results dovetail with prior theoretical definitions of suspicion. We describe implications of this work for HCI, to augment users’ capabilities by enabling them to develop a ‘healthy skepticism’ to parse out truth from fiction online.

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Notes

  1. This was the estimate included in prepared congressional testimony by Facebook on October 30, 2017.

  2. We note concern about the measure of suspicion in this study—which was operationalized as the standard deviation (SD) of buyer price suggestions. That is, two different participants can experience the same SD across trials, yet the means of the suggested values could be lower for one seller (i.e., 1, 2, 3 have the same SD as 11, 12, 13)—and thus, different mean levels can be associated with different levels of suspicion (different levels of feelings of being taken advantage of).

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Acknowledgements

We would like to thank the Air Force Research Laboratory and the Air Force Office of Sponsored Research (FA9550-15-1-0021) for sponsoring this research.

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Authors and Affiliations

  1. Syracuse University, Syracuse, USA

    Leanne Hirshfield, Natalie Sommer & Senem Velipasalar

  2. Virginia Tech, Blacksburg, USA

    Phil Bobko

  3. Illinois State University, Normal, USA

    Alex Barelka

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  1. Leanne Hirshfield
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  2. Phil Bobko
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  4. Natalie Sommer
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Correspondence to Leanne Hirshfield.

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Hirshfield, L., Bobko, P., Barelka, A. et al. Toward Interfaces that Help Users Identify Misinformation Online: Using fNIRS to Measure Suspicion. Augment Hum Res 4, 1 (2019). https://doi.org/10.1007/s41133-019-0011-8

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