Lost in “Transl-Hation”: Exploring the Impact of Machine Translation as an Intermediary Tool in Detecting Armenian Hate Speech
DOI:
https://doi.org/10.46991/TSTP/2023.3.2.040Keywords:
machine translation, hate speech, NLLB, hostile comments, reporting, social mediaAbstract
As the pervasive spread of hate speech continues to pose significant challenges to online communities, detecting, and countering hateful content on social media has become a priority. Social media platforms typically use machine translation to identify the hateful content of the posts made in languages other than English. If this approach works effectively in identifying explicit hateful content in languages that are predominantly used on social media, its effect is almost insignificant when it comes to Armenian.
The present research investigates the effectiveness of machine translation as an intermediary tool in accurately identifying and addressing instances of Armenian hate speech posts retrieved from social networking websites. The study of hate speech posts and comments made by Armenian users in Armenian helps identify that it is often the absence of intricate cultural and linguistic nuances, as well as insufficient contextualized understanding, that impede with hate speech detection in Armenian.
References
Chiang, David. 2007. “Hierarchical Phrase-Based Translation.” Computational Linguistics 33 (2), 201-228.
Hootsuite. 2022. 2022 Digital Trends Report. Accessed January 10, 2023. https://www.hootsuite.com/resources/digital-trends
Huang, Chiao Ling, Zhang, Sining, and Yang, Shu Ching. 2020. “How Students React to Different Cyberbullying Events: Past Experience, Judgment, Perceived Seriousness, Helping Behavior and the Effect of Online Disinhibition.” Computers in Human Behavior, 110, 106338.
Hutson, Elizabeth. 2016. “Cyberbullying in Adolescence”. Advances in Nursing Science, 39(1): 60-70.
Joinson, Adam N. 2001. “Self-Disclosure in Computer-Mediated Communication: The Role of Self-Awareness and Visual Anonymity.” European Journal of Social Psychology 31: 177–192.
Koehn, Philipp, Och, Franz J., and Marcu, Daniel. 2003. “Statistical Phrase-Based Translation.” Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 48-54. Accessed 15 November, 2023. http://aclweb.org/anthology-new/N/N03/N03-1017.pdf.
Meta. No Language Left Behind. Accessed January 12, 2023. https://ai.meta.com/research/no-language-left-behind/
Plato. 2017. “The Republic” (B. Jowett, Trans.) Green World Classics. (Original work published in 375.)
Suler, John. 2003. “The Psychology of Cyberspace”. Accessed 1 September, 2023. www.rider.edu/suler/psycyber/ psy-cyber.html
Suler, John. 2004. “The Online Disinhibition Effect.” CyberPsychology & Behavior. 7 (3): 321–326.
The Guardian, March 11, 2022. Accessed 14 September, 2023. https://www.theguardian.com/technology/2022/mar/11/facebook-and-instagram-let-users-call-for-death-to-russian-soldiers-over-ukraine
Udris, Reinis. 2014. “Cyberbullying among High School Students in Japan: Development and Validation of the Online Disinhibition Scale.” Computers in Human Behavior, 41: 253-261.
Wachs, Sebastian, Wright, Michelle F., and Vazsonyi, Alexander T. 2019. “Understanding the Overlap between Cyberbullying and Cyberhate Perpetration: Moderating Effects of Toxic Online Disinhibition.” Criminal Behaviour and Mental Health, 29(3): 179-188.
Willard, Nancy E. 2007. Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress. Champaign: Research Press.
Wu, Sheng, Lin, Tung-Ching, and Shih, Jou-Fan. 2017. “Examining the Antecedents of Online Disinhibition.” Information Technology & People, 30(1): 189-209.
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Copyright (c) 2023 Lilit Bekaryan
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