research-article

Regressive Saccadic Eye Movements on Fake News

Authors:

Gjergji Kasneci,

Enkelejda KasneciAuthors Info & Claims

ETRA '22: 2022 Symposium on Eye Tracking Research and Applications

Article No.: 7, Pages 1 - 7

https://doi.org/10.1145/3517031.3529619

Published: 08 June 2022 Publication History

Abstract

With the increasing use of the Internet, people encounter a variety of news in online media and social media every day. For digital content without fact-checking mechanisms, it is likely that people perceive fake news as real when they do not have extensive knowledge about the news topic. In this paper, we study human eye movements when reading fake news and real news. Our results suggest that people regress more with their eyes when reading fake news, while the time until the first fixation in the text area of interest is not a distinguishing factor between real and fake content. Our results show that although the truthfulness of the content is not known to people in advance, their visual behavior differs when reading such content, indicating a higher level of confusion when reading fake content.

References

[1]

Hunt Allcott and Matthew Gentzkow. 2017. Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives 31, 2 (2017), 211–36. https://doi.org/10.1257/jep.31.2.211

[2]

Tobias Appel, Peter Gerjets, Stefan Hoffman, Korbinian Moeller, Manuel Ninaus, Christian Scharinger, Natalia Sevcenko, Franz Wortha, and Enkelejda Kasneci. 2021. Cross-task and Cross-participant Classification of Cognitive Load in an Emergency Simulation Game. IEEE Transactions on Affective Computing(2021), 1–1. https://doi.org/10.1109/TAFFC.2021.3098237

Digital Library

[3]

Tobias Appel, Natalia Sevcenko, Franz Wortha, Katerina Tsarava, Korbinian Moeller, Manuel Ninaus, Enkelejda Kasneci, and Peter Gerjets. 2019. Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures. In 2019 International Conference on Multimodal Interaction. ACM, New York, NY, USA, 154–163. https://doi.org/10.1145/3340555.3353735

Digital Library

[4]

Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The Role of Social Networks in Information Diffusion. In Proceedings of the 21st International Conference on World Wide Web. ACM, New York, NY, USA, 519––528. https://doi.org/10.1145/2187836.2187907

Digital Library

[5]

Nilavra Bhattacharya and Jacek Gwizdka. 2018. Relating Eye-Tracking Measures with Changes in Knowledge on Search Tasks. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. ACM, New York, NY, USA, 62:1–62:5. https://doi.org/10.1145/3204493.3204579

Digital Library

[6]

Efe Bozkir, David Geisler, and Enkelejda Kasneci. 2019a. Assessment of Driver Attention during a Safety Critical Situation in VR to Generate VR-Based Training. In ACM Symposium on Applied Perception 2019. ACM, New York, NY, USA, 23:1–23:5. https://doi.org/10.1145/3343036.3343138

Digital Library

[7]

Efe Bozkir, David Geisler, and Enkelejda Kasneci. 2019b. Person Independent, Privacy Preserving, and Real Time Assessment of Cognitive Load using Eye Tracking in a Virtual Reality Setup. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, New York, NY, USA, 1834–1837. https://doi.org/10.1109/VR.2019.8797758

[8]

Efe Bozkir, Philipp Stark, Hong Gao, Lisa Hasenbein, Jens-Uwe Hahn, Enkelejda Kasneci, and Richard Göllner. 2021. Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms. In 2021 IEEE Virtual Reality and 3D User Interfaces (VR). IEEE, New York, NY, USA, 597–605. https://doi.org/10.1109/VR50410.2021.00085

[9]

Klaus Broelemann, Thomas Gottron, and Gjergji Kasneci. 2017. LTD-RBM: Robust and fast latent truth discovery using restricted Boltzmann machines. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, New York, NY, USA, 143–146. https://doi.org/10.1109/ICDE.2017.60

[10]

Dustin P. Calvillo, Bryan J. Ross, Ryan J.B. Garcia, Thomas J. Smelter, and Abraham M. Rutchick. 2020. Political ideology predicts perceptions of the threat of COVID-19 (and susceptibility to fake news about it). Social Psychological and Personality Science 11, 8 (2020), 1119–1128.

[11]

Nora Castner, Enkelejda Kasneci, Thomas Kübler, Katharina Scheiter, Juliane Richter, Thérése Eder, Fabian Hüttig, and Constanze Keutel. 2018. Scanpath Comparison in Medical Image Reading Skills of Dental Students: Distinguishing Stages of Expertise Development. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. ACM, New York, NY, USA, 39:1–39:9. https://doi.org/10.1145/3204493.3204550

Digital Library

[12]

Nadia K. Conroy, Victoria L. Rubin, and Yimin Chen. 2015. Automatic deception detection: Methods for finding fake news. Proceedings of the association for information science and technology 52, 1 (2015), 1–4.

[13]

Correctiv Rechererchen für die Gesellschaft. 2022. Wie erkenne ich Falschmeldungen. https://correctiv.org/faktencheck/wie-erkenne-ich-falschmeldungen/. [Online; accessed 01-February-2022].

[14]

Brendan David-John, Candace Peacock, Ting Zhang, T. Scott Murdison, Hrvoje Benko, and Tanya R. Jonker. 2021. Towards Gaze-Based Prediction of the Intent to Interact in Virtual Reality. In ACM Symposium on Eye Tracking Research and Applications. ACM, New York, NY, USA, 2:1–2:7. https://doi.org/10.1145/3448018.3458008

Digital Library

[15]

Marco L. Della Vedova, Eugenio Tacchini, Stefano Moret, Gabriele Ballarin, Massimo DiPierro, and Luca de Alfaro. 2018. Automatic Online Fake News Detection Combining Content and Social Signals. In 2018 22nd Conference of Open Innovations Association (FRUCT). IEEE, New York, NY, USA, 272–279. https://doi.org/10.23919/FRUCT.2018.8468301

[16]

Michael A. Eskenazi and Jocelyn R. Folk. 2017. Regressions during reading: The cost depends on the cause. Psychonomic Bulletin & Review 24, 4 (2017), 1211–1216. https://doi.org/10.3758/s13423-016-1200-9

[17]

Hong Gao, Efe Bozkir, Lisa Hasenbein, Jens-Uwe Hahn, Richard Göllner, and Enkelejda Kasneci. 2021. Digital Transformations of Classrooms in Virtual Reality. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 483:1–483:10. https://doi.org/10.1145/3411764.3445596

Digital Library

[18]

Aline Godfroid, Jieun Ahn, Ina Choi, Laura Ballard, Yaqiong Cui, Suzanne Johnston, Shinhye Lee, Abdhi Sarkar, and Hyung-Jo Yoon. 2018. Incidental vocabulary learning in a natural reading context: An eye-tracking study. Bilingualism: Language and Cognition 21, 3 (2018), 563–584.

[19]

Steffen Gottschling and Yvonne Kammerer. 2021. Readers’ Regulation and Resolution of a Scientific Conflict Based on Differences in Source Information: An Eye-Tracking Study. Discourse Processes 58, 5-6 (2021), 468–490. https://doi.org/10.1080/0163853X.2021.1930808

[20]

Adrien Guille, Hakim Hacid, Cecile Favre, and Djamel A Zighed. 2013. Information diffusion in online social networks: A survey. ACM SIGMOD Record 42, 2 (2013), 17–28. https://doi.org/10.1145/2503792.2503797

Digital Library

[21]

Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Birger Larsen, Stephen Alstrup, and Christina Lioma. 2020. Factuality Checking in News Headlines with Eye Tracking. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, 2013–2016. https://doi.org/10.1145/3397271.3401221

Digital Library

[22]

Stefan Helmstetter and Heiko Paulheim. 2018. Weakly Supervised Learning for Fake News Detection on Twitter. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, New York, NY, USA, 274–277. https://doi.org/10.1109/ASONAM.2018.8508520

[23]

Maximilian Jenders, Gjergji Kasneci, and Felix Naumann. 2013. Analyzing and Predicting Viral Tweets. In Proceedings of the 22nd International Conference on World Wide Web. ACM, New York, NY, USA, 657––664. https://doi.org/10.1145/2487788.2488017

Digital Library

[24]

Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, and Thore Graepel. 2010. Bayesian knowledge corroboration with logical rules and user feedback. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 1–18. https://doi.org/10.1007/978-3-642-15883-4_1

Digital Library

[25]

Natalia N. Kovaleva, S.A. Anichkin, and A.S. Anisimova. 2020. Legal Aspects of Information Threats in the Form of” Fakes” in the Conditions of Spread of COVID-19. In Research Technologies of Pandemic Coronavirus Impact (RTCOV 2020). Atlantis Press, Paris, France, 222–226.

[26]

Krzysztof Krejtz, Andrew T. Duchowski, Anna Niedzielska, Cezary Biele, and Izabela Krejtz. 2018. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PLOS ONE 13, 9 (2018), 1–23. https://doi.org/10.1371/journal.pone.0203629

[27]

Howard Levene. 1960. Robust tests for equality of variances. Contributions to probability and statistics: essays in honor of Harold Hotelling (1960), 278–292.

[28]

Henry B. Mann and Donald R. Whitney. 1947. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics 18, 1 (1947), 50–60.

[29]

Kenneth O. McGraw and Seok P. Wong. 1992. A common language effect size statistic.Psychological bulletin 111, 2 (1992), 361. https://doi.org/10.1037/0033-2909.111.2.361

[30]

Joshua Newn, Eduardo Velloso, Fraser Allison, Yomna Abdelrahman, and Frank Vetere. 2017. Evaluating Real-Time Gaze Representations to Infer Intentions in Competitive Turn-Based Strategy Games. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play. ACM, New York, NY, USA, 541–552. https://doi.org/10.1145/3116595.3116624

Digital Library

[31]

Mattias Nilsson Benfatto, Gustaf Öqvist Seimyr, Jan Ygge, Tony Pansell, Agneta Rydberg, and Christer Jacobson. 2016. Screening for Dyslexia Using Eye Tracking during Reading. PLOS ONE 11, 12 (12 2016), 1–16. https://doi.org/10.1371/journal.pone.0165508

[32]

Anneli Olsen. 2012. The Tobii I-VT fixation filter., 21 pages. Tobii Technology.

[33]

Gordon Pennycook and David G. Rand. 2019. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188(2019), 39–50. https://doi.org/10.1016/j.cognition.2018.06.011

[34]

Gordon Pennycook and David G. Rand. 2020. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality 88, 2 (2020), 185–200. https://doi.org/10.1111/jopy.12476

[35]

Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2017. Automatic Detection of Fake News. arxiv:1708.07104 [cs.CL]

[36]

Keith Rayner. 1998. Eye movements in reading and information processing: 20 years of research.Psychological bulletin 124, 3 (1998), 372.

[37]

Julio C. S. Reis, André Correia, Fabrício Murai, Adriano Veloso, and Fabrício Benevenuto. 2019a. Explainable Machine Learning for Fake News Detection. In Proceedings of the 10th ACM Conference on Web Science. ACM, New York, NY, USA, 17–26. https://doi.org/10.1145/3292522.3326027

Digital Library

[38]

Julio C. S. Reis, André Correia, Fabrício Murai, Adriano Veloso, and Fabrício Benevenuto. 2019b. Supervised Learning for Fake News Detection. IEEE Intelligent Systems 34, 2 (2019), 76–81. https://doi.org/10.1109/MIS.2019.2899143

Digital Library

[39]

Yao Rong, Wenjia Xu, Zeynep Akata, and Enkelejda Kasneci. 2021. Human Attention in Fine-grained Classification. In British Machine Vision Conference (BMVC). 1–14. https://arxiv.org/abs/2111.01628.

[40]

Ahmed Sajjad, Knut Hinkelmann, and Flavio Corradini. 2019. Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey. In Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). AAAI Press, Palo Alto, CA, USA, 1–12.

[41]

Ladislao Salmerón, Pablo Delgado, and Lucia Mason. 2020. Using eye-movement modelling examples to improve critical reading of multiple webpages on a conflicting topic. Journal of Computer Assisted Learning 36, 6 (2020), 1038–1051. https://doi.org/10.1111/jcal.12458

[42]

Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, and Carolina Scarton. 2020. Measuring the Impact of Readability Features in Fake News Detection. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 1404–1413. https://aclanthology.org/2020.lrec-1.176

[43]

Elizabeth R. Schotter, Randy Tran, and Keith Rayner. 2014. Don’t believe what you read (only once) comprehension is supported by regressions during reading. Psychological science 25, 6 (2014), 1218–1226.

[44]

Anne Schüler. 2017. Investigating gaze behavior during processing of inconsistent text-picture information: Evidence for text-picture integration. Learning and Instruction 49 (2017), 218–231. https://doi.org/10.1016/j.learninstruc.2017.03.001

[45]

Samuel S. Shapiro and Martin B. Wilk. 1965. An analysis of variance test for normality (complete samples). Biometrika 52, 3-4 (1965), 591–611. https://doi.org/10.1093/biomet/52.3-4.591

[46]

Heather Sheridan, Kinnera S. Maturi, and Abigail L. Kleinsmith. 2020. Chapter Five - Eye movements during music reading: Toward a unified understanding of visual expertise. In Gazing Toward the Future: Advances in Eye Movement Theory and Applications, Kara D. Federmeier and Elizabeth R. Schotter (Eds.). Psychology of Learning and Motivation, Vol. 73. Academic Press, Cambridge, MA, USA, 119–156. https://doi.org/10.1016/bs.plm.2020.07.002

[47]

Frederick Shic. 2016. Eye Tracking as a Behavioral Biomarker for Psychiatric Conditions: The Road Ahead. Journal of the American Academy of Child & Adolescent Psychiatry 55, 4(2016), 267–268. https://doi.org/10.1016/j.jaac.2016.02.002

[48]

Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter 19, 1 (2017), 22–36. https://doi.org/10.1145/3137597.3137600

Digital Library

[49]

John L. Sibert, Mehmet Gokturk, and Robert A. Lavine. 2000. The reading assistant: eye gaze triggered auditory prompting for reading remediation. In Proceedings of the 13th annual ACM symposium on User interface software and technology. ACM, New York, NY, USA, 101–107.

Digital Library

[50]

Jakub Simko, Martina Hanakova, Patrik Racsko, Matus Tomlein, Robert Moro, and Maria Bielikova. 2019. Fake News Reading on Social Media: An Eye-Tracking Study. In Proceedings of the 30th ACM Conference on Hypertext and Social Media. ACM, New York, NY, USA, 221–230. https://doi.org/10.1145/3342220.3343642

Digital Library

[51]

Cornelia Sindermann, Andrew Cooper, and Christian Montag. 2020. A short review on susceptibility to falling for fake political news. Current Opinion in Psychology 36 (2020), 44–48. https://doi.org/10.1016/j.copsyc.2020.03.014

[52]

Ömer Sümer, Efe Bozkir, Thomas Kübler, Sven Grüner, Sonja Utz, and Enkelejda Kasneci. 2021. FakeNewsPerception: An eye movement dataset on the perceived believability of news stories. Data in Brief 35(2021), 106909. https://doi.org/10.1016/j.dib.2021.106909

[53]

Tobii AB. 2022. Tobii Pro Spectrum. https://www.tobiipro.com/product-listing/tobii-pro-spectrum/. [Online; last accessed 26-Jan-2022].

[54]

Raphael Vallat. 2018. Pingouin: statistics in Python. Journal of Open Source Software 3, 31 (2018), 1026. https://doi.org/10.21105/joss.01026

[55]

Jay J. Van Bavel and Andrea Pereira. 2018. The Partisan Brain: An Identity-Based Model of Political Belief. Trends in Cognitive Sciences 22, 3 (2018), 213–224. https://doi.org/10.1016/j.tics.2018.01.004

[56]

András Vargha and Harold D. Delaney. 2000. A Critique and Improvement of the ”CL” Common Language Effect Size Statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics 25, 2(2000), 101–132.

[57]

Mélodie Vidal, Jayson Turner, Andreas Bulling, and Hans Gellersen. 2012. Wearable eye tracking for mental health monitoring. Computer Communications 35, 11 (2012), 1306–1311. https://doi.org/10.1016/j.comcom.2011.11.002

Digital Library

[58]

Françoise Vitu, George W. McConkie, and David Zola. 1998. Chapter 5 - About Regressive Saccades in Reading and Their Relation to Word Identification. In Eye Guidance in Reading and Scene Perception, Geoffrey Underwood (Ed.). Elsevier Science Ltd., Amsterdam, The Netherlands, 101–124. https://doi.org/10.1016/B978-008043361-5/50006-7

[59]

Dong Wang, Lance Kaplan, Hieu Le, and Tarek Abdelzaher. 2012. On truth discovery in social sensing: A maximum likelihood estimation approach. In 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN). IEEE, New York, NY, USA, 233–244. https://doi.org/10.1109/IPSN.2012.6920960

Digital Library

[60]

Xiaoxin Yin, Jiawei Han, and S Yu Philip. 2008. Truth discovery with multiple conflicting information providers on the web. IEEE transactions on knowledge and data engineering 20, 6(2008), 796–808.

[61]

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, and Jiawei Han. 2012. A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. Proceedings of the VLDB Endowment 5, 6 (2012), 550–561. https://doi.org/10.14778/2168651.2168656

Digital Library

Cited By

Bozkir ERiedmiller CSkodras AKasneci GKasneci E(2024)Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by HumansACM Transactions on Applied Perception10.1145/369666722:2(1-23)Online publication date: 30-Nov-2024
https://dl.acm.org/doi/10.1145/3696667
Hienert DSchmidt HKrämer TKern D(2024)EyeLiveMetrics: Real-time Analysis of Online Reading with Eye TrackingProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3656495(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3656495
Matthee MHattingh MNormund-van Wyk L(2024)The use of heat maps to explore perceived visual indicators of online fake newsProcedia Computer Science10.1016/j.procs.2024.06.346239(1687-1695)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.346
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cover image ACM Conferences

ETRA '22: 2022 Symposium on Eye Tracking Research and Applications

June 2022

408 pages

ISBN:9781450392525

DOI:10.1145/3517031

Editors:
Frederick Shic
University of Washington, USA
,
Enkelejda Kasneci
University of Tübingen, Germany
,
Mohamed Khamis
University of Glasgow, Scotland
,
Hans Gellersen
Aarhus University, Denmark
,
Enkelejda Kasneci
University of Tübingen, Germany
,
Krzysztof Krejtz
SWPS University of Social Sciences and Humanities, Poland
,
Daniel Weiskopf
University of Stuttgart, Germany
,
Tanja Blascheck
Stuttgart University, Germany
,
Jessica Bradshaw
University of South Carolina, USA
,
Hana Vrzakova
University of Eastern Finland, Finland
,
Kamran Binaee
University of Nevada, USA
,
Michael Burch
University of Applied Sciences in Chur, Switzerland
,
Peter Kiefer
ETH Zurich, Switzerland
,
Roman Bednarik
University of Eastern Finland
,
Diako Mardanbegi
AdHawk Microsystems, Canada
,
Christopher Clarke
University of Bath, Chur, England
,
Rakshit Kothari
Rochester Institute of Technology, USA
,
Vijay Rajanna
Senior Research Engineer at Sensel, USA
,
Sampath Jayarathna
Old Dominion University, USA
,
Arantxa Villanueva
Public University of Navarra, Spain
,
Adham Atyabi
University of Colorado, USA
,
Shahram Eivazi
University of Tübingen, Germany

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June 8 - 11, 2022

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Cited By

Bozkir ERiedmiller CSkodras AKasneci GKasneci E(2024)Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by HumansACM Transactions on Applied Perception10.1145/369666722:2(1-23)Online publication date: 30-Nov-2024
https://dl.acm.org/doi/10.1145/3696667
Hienert DSchmidt HKrämer TKern D(2024)EyeLiveMetrics: Real-time Analysis of Online Reading with Eye TrackingProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3656495(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3656495
Matthee MHattingh MNormund-van Wyk L(2024)The use of heat maps to explore perceived visual indicators of online fake newsProcedia Computer Science10.1016/j.procs.2024.06.346239(1687-1695)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.346
Steinfeld N(2023)How Do Users Examine Online Messages to Determine If They Are Credible? An Eye-Tracking Study of Digital Literacy, Visual Attention to Metadata, and Success in Misinformation IdentificationSocial Media + Society10.1177/205630512311968719:3Online publication date: 26-Sep-2023
https://doi.org/10.1177/20563051231196871

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