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A verdade sob a superfície: Para quantificar e entender a avaliação do discurso de ódio alemão e dinamarquês com biosinais de EEG
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Discurso de ódio

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1.
Niebuhr O, Neitsch J. A verdade sob a superfície: Para quantificar e entender a avaliação do discurso de ódio alemão e dinamarquês com biosinais de EEG. J. of Speech Sci. [Internet]. 14º de novembro de 2022 [citado 28º de janeiro de 2026];11(00):e022004. Disponível em: https://econtents.sbu.unicamp.br/inpec/index.php/joss/article/view/16153

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Resumo

O receptor é um fator externo que foi pouco investigado em pesquisas sobre discurso de ódio. No entanto, abordar esse fator é essencial para entender como e por que esse discurso desdobra seus efeitos negativos e quais características do destinatário influenciam esses efeitos. Este estudo tem como foco o destinatário. Com base em descobertas anteriores de classificações explícitas e replicações iniciais bem-sucedidas de tais classificações por meio de biosinais, apresentamos o primeiro estudo sistemático e interlinguístico sobre discurso de ódio com base em duas medidas de EEG: a proporção beta-alfa associada a excitação e a assimetria alfa frontal associada à valência. Cinquenta participantes dinamarqueses e alemães participaram e foram apresentados a estímulos de discurso de ódio falados e escritos de postagens autênticas de discurso de ódio no Twitter. Os resultados mostram que os dinamarqueses reagiram de forma mais sensível do que os alemães ao discurso contendo linguagem figurada (palavrões), enquanto os alemães reagiram de forma mais sensível ao discurso com referências ao Holocausto do que os dinamarqueses. Além disso, professores e advogados mostraram menos reações negativas a esse discurso do que funcionários da igreja, estudantes e aposentados. O efeito do meio de apresentação dependeu do respectivo tipo de discurso. Em particular, denunciar o discurso de ódio com base na ironia e indiretas atenuou seus efeitos sobre os destinatários sendo questionável se os estímulos ainda eram percebidos como discurso de ódio. Discutimos os resultados e suas implicações práticas para a punição e gestão do discurso de ódio nas redes sociais.

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Referências

Asif A, Majid M, Anwar SM. Human stress classification using EEG signals in response to music tracks. Computers in Biology and Medicine. 2019; 107: 182-196.

Balcerzak B, Jaworski W. Application of linguistic cues in the analysis of language of hate groups. Computer Science. 2015; 16.

Baumgarten N, Bick E, Geyer K, Iversen DA, Kleene A, Lindø AV, ... Petersen EN. Towards balance and boundaries in public discourse: expressing and perceiving online hate speech (XPEROHS). International Journal of Language and Communication. 2019; 50: 87-108.

Beer J, Beer J, Markley RP, Camp CJ. Age and living conditions as related to perceptions of ambiguous figures. Psychol. Rep. 1989; 64: 1027–1033.

Bialystok E, Shapero D. Ambiguous benefits: The effect of bilingualism on reversing ambiguous figures. Developmental Science. 2005; 8: 595-604.

Bick E. An Annotated Social Media Corpus for German. Proc. 12th International Conference on Language Resources and Evaluation, Marseille, France. 2020: 6127-6135.

Bick E, Geyer K, Kleene A. „Die ách so friedlichen Muslime “: Eine korpusbasierte Untersuchung von Formulierungsmustern fremdenfeindlicher Aussagen in Sozialen Medien. In Wachs S, Koch-Priewe B, Zick, A, editors. Hate Speech-Multidisziplinäre Analysen und Handlungsoptionen. Wiesbaden: Springer; 2021. pp. 81-103.

Bregman, AS. Auditory scene analysis: The perceptual organization of sound. Cambridge: MIT press; 1994.

Calderón FH., Balani N, Taylor J, Peignon M, Huang YH, Chen YS. Linguistic Patterns for Code Word Resilient Hate Speech Identification. Sensors. 2021; 21: 7859.

Cao T, Wang L, Sun Z, Engel SA, & He S.. The independent and shared mechanisms of intrinsic brain dynamics: Insights from bistable perception. Frontiers in Psychology. 2018; 9: 589.

Carroll EA., Latulipe C, Fung R, Terry M. Creativity factor evaluation: towards a standardized survey metric for creativity support. Proceedings of the seventh ACM conference on Creativity and cognition. 2009: 127-136.

Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. Proc. 11th International AAAI Conference on Web and Social Media ICWSM '17. 2017: 512-515.

D’Errico F, Signorello R, Demolin D, Poggi I. The Perception of Charisma from Voice: A Cross-Cultural Study. Proc. Humaine Association Conference on Affective Computing and Intelligent Interaction. 2013: 552-557.

Fodor, JD. Psycholinguistics Cannot Escape Prosody. Proc. 1st International Conference on Speech Prosody, Aix-en-Provence, France. 2002: 83–88.

Fortuna P, Nunes S. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR). 2018; 51: 1-30.

Gale AG, Findlay JM. Eye movement patterns in viewing ambiguous figures. Eye movements and psychological functions: International views. 1983: 145-168.

Gambäck B, Sikdar UK. Using convolutional neural networks to classify hate-speech. Proc. 1st Workshop on Abusive Language Online. 2017: 85-90.

García-Acosta A., Riva-Rodríguez JDL, Sánchez-Leal J, Reyes-Martínez RM. Neuroergonomic Stress Assessment with Two Different Methodologies in a Manual Repetitive Task-Product Assembly. Computational Intelligence and Neuroscience. 2021.

Garcia-Moreno FM, Bermudez-Edo M, Garrido JL, Rodríguez-Fórtiz MJ. Reducing response time in motor imagery using a headband and deep learning. Sensors. 2020; 20: 6730.

Gentile DA, Woodhouse J, Lynch P, Maier J, McJunkin T. Reliability and validity of the Global Pain Scale with chronic pain sufferers. Pain Physician. 2011; 14: 61-70.

Geyer K. Entmenschlichende Metaphern in ethnotroper („fremdenfeindlicher“) Hatespeech in sozialen Medien. In Bülow L, Marx K, Meyer-Vieracker S, Mroczynski, R, editors. Digitale Pragmatik. Heidelberg: J. B. Metzler; 2021.

Geyer K, Bick E, Kleene A. “I am not a racist, but …”. A Corpus-Based Analysis of Xenophobic Hate Speech Constructions in Danish and German Social Media Discourse. In Knoblock N, editor. Grammar of Hate: Morphosyntactic Features of Hateful, Aggressive, and Dehumanizing Discourse. Cambridge: Cambridge University Press; 2021.

Geyer K. Die ‚Grammatik‘ der Hassrede – am Beispiel des Dänischen. In Strässler J. editor. Sprache(n) für Europa. Mehrsprachigkeit als Chance. Frankfurt: Peter Lang; 2019. pp. 195-207.

Goldstein, EB. Blackwell handbook of sensation and perception. John Wiley & Sons; 2008.

Handel S. Listening: an Introduction to the Perception of Auditory Events. Cambridge: MIT Press; 1989.

Herman K, Ciechanowski L, Przegalińska A. Emotional well-being in urban wilderness: Assessing states of calmness and alertness in informal green spaces (IGSs) with muse—Portable EEG headband. Sustainability. 2021; 13: 2212.

Hrdina M. Identity, activism and hatred: Hate speech against migrants on Facebook in the Czech Republic in 2015. Naše společnost. 2016; 14: 38–47.

Jaki S, De Smedt T. Right-wing German hate speech on Twitter: Analysis and automatic detection. arXiv preprint arXiv:1910.07518. 2019.

Klem GH, Lüders HO, Jasper HH, Elger C. The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalography and Clinical Neurophysiology, Supplement. 1999; 52: 3–6.

Klintman H. Original thinking and ambiguous figure reversal rates. Bulletin of the Psychonomic Society. 1984; 22: 129-131.

LaRocco J, Le Minh D, Paeng D-G. A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection. Frontiers in Neuroinformatics. 2020; 14: 1-42.

Laukkonen RE, Tangen JM. Can observing a Necker cube make you more insightful? Consciousness and Cognition. 2017; 48: 198-211

Long GM, Toppino, TC. Multiple representations of the same reversible figure: Implications for cognitive decisional interpretations. Perception. 1981; 10: 231-234.

Levisen C. Dark, but Danish: Ethnopragmatic perspectives on black humor. Intercultural Pragmatics. 2018; 15: 515-531.

Malmasi S, Zampieri M. Detecting hate speech in social media. arXiv preprint arXiv:1712.06427. 2017.

MacAvaney S, Yao HR, Yang E, Russell K, Goharian N, Frieder O. Hate speech detection: Challenges and solutions. PloS one. 2019; 14: e0221152.

Martins R, Gomes M, Almeida JJ, Novais P, Henriques P. Hate speech classification in social media using emotional analysis. Proc. 7th Brazilian Conference on Intelligent Systems (BRACIS), IEEE. 2018: 61-66.

Neitsch J, Niebuhr O. Assessing hate-speech perception through bio-signal measurements: A pilot study. Proc. Biosignale 2020, Kiel, Germany, 2020: 66-67.

Neitsch J, Niebuhr O, Kleene A. What if hate speech really was speech? Towards explaining hate speech in a cross-modal approach. In Wachs S, Koch-Priewe B, Zick, A, editors. Hate Speech-Multidisziplinäre Analysen und Handlungsoptionen. Wiesbaden: Springer; 2021. pp. 105-135.

Neitsch J, Niebuhr O. Types of hate speech: How speakers of Danish rate spoken vs. written hate speech. Proc. 4th International Conference of Phonetics and Phonology in Europe, Barcelona, Spain. 2021: 1-2.

Nielsen PA. Choice of Law for Defamation, Privacy Rights and Freedom of Speech. Oslo Law Review. 2019; 6: 32-42.

Papcunová J, Martončik M, Fedáková D, Kentoš M, Bozogáňová M, Srba I, ... Adamkovič M. Hate speech operationalization: a preliminary examination of hate speech indicators and their structure. Complex & Intelligent Systems. 2021: 1-16.

Peters MA. Limiting the capacity for hate: Hate speech, hate groups and the philosophy of hate. Educational Philosophy and Theory. 2020: 1-6.

Richer R, Zhao N, Amores J, Eskofier BM, Paradiso JA. Real-time mental state recognition using a wearable EEG. Proc. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, USA. 2018: 5495-5498.

Roberts R, Woodman T. Personality and performance: Moving beyond the Big 5. Current Opinion In Psychology. 2017; 16: 104-108.

Rodríguez-Martínez GA, Castillo-Parra H. Bistable perception: neural bases and usefulness in psychological research. International Journal of Psychological Research. 2018; 11: 63-76.

Ruwandika NDT, Weerasinghe AR. Identification of hate speech in social media. Proc. 18th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE. 2018: 273-278.

Shechter S, Hillman P, Hochstein S, Shapley RM. Gender differences in apparent motion perception. Perception. 1991; 20: 307–314.

Waseem Z, Hovy D. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. Proc. NAACL Student Res. Work. 2016: 88-93.

Zhang X, Bachmann P, Schilling TM, Naumann E, Schächinger H, Larra, MF. Emotional stress regulation: The role of relative frontal alpha asymmetry in shaping the stress response. Biological psychology. 2018; 138: 231-239.

Zhao G, Zhang Y, Ge Y. Frontal EEG asymmetry and middle line power difference in discrete emotions. Frontiers in behavioral neuroscience. 2018; 12: 225.

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Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Copyright (c) 2022 Oliver Niebuhr, Jana Neitsch

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