Exploring Deceptive Patterns: Insights from Eye Tracking, EMG and Sentiment Analysis

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Exploring Deceptive Patterns: Insights from Eye Tracking, EMG and Sentiment Analysis

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Author(s): Marzieh Jamalifard , Dr Tony Russell-Rose , Dr Harry Brignull

Publication date (Print): July 2024

Conference name: 37th International BCS Human-Computer Interaction Conference (BCS HCI 24)

Conference theme: The International BCS Human-Computer Interaction Conference in 2024 was supported by the BCS Interactions Special Interest Group and hosted by the University of Central Lancashire in Preston. The BCS HCI Conference welcomed submissions on all aspects of human-computer interaction. Topics included: user experience (UX), usability testing, interaction design (IxD), human-centred AI (HCAI), education, health, sustainability, the Internet of Things (IoT), interaction technologies, and emerging interactive applications.

Conference date: 15–17 July 2024

Keywords: Deceptive Pattern, User experience, Eye tracking, EMG analysis

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Marzieh Jamalifard

Dr Tony Russell-Rose

Dr Harry Brignull

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Publication date: July 2024

Publication date (Print): July 2024

Pages: 213-218

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[0001]Goldsmiths, University of London

Computing Department

London SE146NWUK

[0002]Testimonium Ltd

Unit 219, Foundry 78 The Beacon

Eastbourne, BN21 3NW UK

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DOI: 10.14236/ewic/BCSHCI2024.20

SO-VID: a53ac507-a805-423d-b8f0-3a78bf67c4aa

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 37th International BCS Human-Computer Interaction Conference

Conference acronym: BCS HCI 24

Conference number: 37

Conference location: University of Central Lancashire (UCLan)

Conference date: 15–17 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/BCSHCI2024.20

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

BirdS., KleinE., and LoperE. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. ” O’Reilly Media, Inc.”.
BrignullH. (2023). Deceptive patterns: Exposing the tricks tech companies use to control you. (No Title).
CacioppoJ. T., PettyR.E., LoschM.E., and KimH.S. (1986). Electromyographic activity over facial muscle regions can differentiate the valence and intensity of affective reactions. Journal of personality and social psychology 50(2), 260.
Di GeronimoL., BrazL., FregnanE., PalombaF., and BacchelliA. (2020). Ui dark patterns and where to find them: a study on mobile applications and user perception. In Proceedings of the 2020 CHI conference on human factors in computing systems, pp. 1–14.
GrayC. M., KouY., BattlesB., HoggattJ., and ToombsA.L. (2018). The dark (patterns) side of ux design. In Proceedings of the 2018 CHI conference on human factors in computing systems, pp. 1–14.
HassenzahlM. and SandwegN. (2004). From mental effort to perceived usability: transforming experiences into summary assessments. In CHI’04 extended abstracts on Human factors in computing systems, pp. 1283–1286.
IbarraI. A. (2017). Should we treat data as labor? Moving beyond’free’. SSRN.
KaleS. N. and DudulS.V. (2009). Intelligent noise removal from emg signal using focused timelagged recurrent neural network. Applied Computational Intelligence and Soft Computing 2009(1), 129761.
KellarM., HawkeyK., InkpenK.M., and WattersC. (2008). Challenges of capturing natural webbased user behaviors. Intl. Journal of Human–Computer Interaction 24(4), 385–409.
LuguriJ. and StrahilevitzL.J. (2021). Shining a light on dark patterns. Journal of Legal Analysis 13(1), 43–109.
Lupi´a˜ nez-VillanuevaF., BoludaA., BogliacinoF., LivaG., LechardoyL., and deT.R. las Heras Ballell (2022). Behavioural study on unfair commercial practices in the digital environment: dark patterns and manipulative personalisation. Publications Office of the European Union.
LvH.-R., Z.-LinL., W.-YinJ., and DongJ. (2008). Emotion recognition based on pressure sensor keyboards. In 2008 IEEE international conference on multimedia and expo, pp. 1089–1092. IEEE.
M. BhootA., ShindeM.A., and MishraW.P. (2020). Towards the identification of dark patterns: An analysis based on end-user reactions. In Proceedings of the 11th Indian Conference on Human-Computer Interaction, pp. 24–33.
MathurA., AcarG., FriedmanM.J., LucheriniE., MayerJ., ChettyM., and NarayananA. (2019). Dark patterns at scale: Findings from a crawl of 11k shopping websites. Proceedings of the ACM on human-computer interaction 3(CSCW), 1–32. McCarthyJ. and WrightP. (2004). Technology as experience. interactions 11(5), 42–43.
OuztsA. D. and DuchowskiA.T. (2012). Comparison of eye movement metrics recorded at different sampling rates. In Proceedings of the symposium on eye tracking research and applications, pp. 321–324.
RutkowskaJ. M., GhilardiT., VacaruS.V., vanJ.E. Schaik, MeyerM., HunniusS., and OostenveldR. (2024). Optimal processing of surface facial emg to identify emotional expressions: A data-driven approach. Behavior Research Methods, 1–14.
SchumannN. P., BongersK., GuntinasO.-Lichius, and ScholleH.C. (2010). Facial muscle activation patterns in healthy male humans: A multi-channel surface emg study. Journal of neuroscience methods 187(1), 120–128.
Van BoxtelA. (2001). Optimal signal bandwidth for the recording of surface emg activity of facial, jaw, oral, and neck muscles. Psychophysiology 38(1), 22–34.
ZacA., Y.-HuangC., vonA. Moltke, DeckerC., and EzrachiA. (2023). Dark patterns and online consumer vulnerability. Available at SSRN 4547964.

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Comment on this article

[1] BirdS., KleinE., and LoperE. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. ” O’Reilly Media, Inc.”.

[2] BrignullH. (2023). Deceptive patterns: Exposing the tricks tech companies use to control you. (No Title).

[3] CacioppoJ. T., PettyR.E., LoschM.E., and KimH.S. (1986). Electromyographic activity over facial muscle regions can differentiate the valence and intensity of affective reactions. Journal of personality and social psychology 50(2), 260.

[4] Di GeronimoL., BrazL., FregnanE., PalombaF., and BacchelliA. (2020). Ui dark patterns and where to find them: a study on mobile applications and user perception. In Proceedings of the 2020 CHI conference on human factors in computing systems, pp. 1–14.

[5] GrayC. M., KouY., BattlesB., HoggattJ., and ToombsA.L. (2018). The dark (patterns) side of ux design. In Proceedings of the 2018 CHI conference on human factors in computing systems, pp. 1–14.

[6] HassenzahlM. and SandwegN. (2004). From mental effort to perceived usability: transforming experiences into summary assessments. In CHI’04 extended abstracts on Human factors in computing systems, pp. 1283–1286.

[7] IbarraI. A. (2017). Should we treat data as labor? Moving beyond’free’. SSRN.

[8] KaleS. N. and DudulS.V. (2009). Intelligent noise removal from emg signal using focused timelagged recurrent neural network. Applied Computational Intelligence and Soft Computing 2009(1), 129761.

[9] KellarM., HawkeyK., InkpenK.M., and WattersC. (2008). Challenges of capturing natural webbased user behaviors. Intl. Journal of Human–Computer Interaction 24(4), 385–409.

[10] LuguriJ. and StrahilevitzL.J. (2021). Shining a light on dark patterns. Journal of Legal Analysis 13(1), 43–109.

[11] Lupi´a˜ nez-VillanuevaF., BoludaA., BogliacinoF., LivaG., LechardoyL., and deT.R. las Heras Ballell (2022). Behavioural study on unfair commercial practices in the digital environment: dark patterns and manipulative personalisation. Publications Office of the European Union.

[12] LvH.-R., Z.-LinL., W.-YinJ., and DongJ. (2008). Emotion recognition based on pressure sensor keyboards. In 2008 IEEE international conference on multimedia and expo, pp. 1089–1092. IEEE.

[13] M. BhootA., ShindeM.A., and MishraW.P. (2020). Towards the identification of dark patterns: An analysis based on end-user reactions. In Proceedings of the 11th Indian Conference on Human-Computer Interaction, pp. 24–33.

[14] MathurA., AcarG., FriedmanM.J., LucheriniE., MayerJ., ChettyM., and NarayananA. (2019). Dark patterns at scale: Findings from a crawl of 11k shopping websites. Proceedings of the ACM on human-computer interaction 3(CSCW), 1–32. McCarthyJ. and WrightP. (2004). Technology as experience. interactions 11(5), 42–43.

[15] OuztsA. D. and DuchowskiA.T. (2012). Comparison of eye movement metrics recorded at different sampling rates. In Proceedings of the symposium on eye tracking research and applications, pp. 321–324.

[16] RutkowskaJ. M., GhilardiT., VacaruS.V., vanJ.E. Schaik, MeyerM., HunniusS., and OostenveldR. (2024). Optimal processing of surface facial emg to identify emotional expressions: A data-driven approach. Behavior Research Methods, 1–14.

[17] SchumannN. P., BongersK., GuntinasO.-Lichius, and ScholleH.C. (2010). Facial muscle activation patterns in healthy male humans: A multi-channel surface emg study. Journal of neuroscience methods 187(1), 120–128.

[18] Van BoxtelA. (2001). Optimal signal bandwidth for the recording of surface emg activity of facial, jaw, oral, and neck muscles. Psychophysiology 38(1), 22–34.

[19] ZacA., Y.-HuangC., vonA. Moltke, DeckerC., and EzrachiA. (2023). Dark patterns and online consumer vulnerability. Available at SSRN 4547964.

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Exploring Deceptive Patterns: Insights from Eye Tracking, EMG and Sentiment Analysis

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