Shynar Mussiraliyeva
ABSTRACT
The work describes various manifestations of ageism, including stereotypes, prejudice, discrimination and marginalization. As a social phenomenon, ageism can have serious negative consequences for both individuals and society as a whole, including low self-esteem, social isolation and limited opportunities for older people. The study highlights the importance of fighting ageism and calls for understanding and respect for people of all ages. The study also reveals the presence of ageism on the Internet and emphasizes the need to identify and eliminate it in the digital environment, including websites, social platforms and advertising. Online ageism can take many forms, and the fight against it requires attention to stereotypes and bias on digital platforms and contributes to the fact that each age group is a society that is valued and respected, emphasizing the importance of respect for age.
Since the research is devoted to the problem of identifying and classifying cyber-threatening texts on Kazakhstani web resources, such as ageism, ableism and social discrimination, data from social networks were collected using Ari technologies and analytical programs. The datasets include texts from three different categories. Various machine-learning algorithms were used to detect and classify cyber threats, such as random forest, support vector machine, decision tree methods, Gaussian naive Bayes, gradient ascent methods and logistic regression. The effectiveness of the text classification model was evaluated using Accuracy, Precision, Recall and F1 indicators.
This work highlights the importance of identifying and combating social discrimination on social media, focusing on privacy, ethics and bias when developing cybersecurity detection tools.
Thus, this research contributes to improving the security and ethics of social networks, protecting vulnerable groups from cyber threats and compliance with norms and laws in the online space.
- Marques S, Mariano J, Mendonça J, De Tavernier W, Hess M, Naegele L, Peixeiro F, Martins D. Determinants of Ageism against Older Adults: A Systematic Review. Int J Environ Res Public Health. 2020 Apr 8;17(7):2560. doi: 10.3390/ijerph17072560. PMID: 32276489; PMCID: PMC7178234.Google Scholar
Cross Ref
- Comincioli E, Hakoköngäs E, Masoodian M. Identifying and Addressing Implicit Ageism in the Co-Design of Services for Aging People. Int J Environ Res Public Health. 2022 Jun 23;19(13):7667. doi: 10.3390/ijerph19137667. PMID: 35805326; PMCID: PMC9265871.Google ScholarCross Ref
- Swift, Hannah & Abrams, Dominic & Lamont, Ruth & Drury, Libby. (2017). The Risks of Ageism Model: How Ageism and Negative Attitudes toward Age Can Be a Barrier to Active Aging: Risks of Ageism Model. Social Issues and Policy Review. 11. 195-231. 10.1111/sipr.12031.Google ScholarCross Ref
- Tom Bourgeade, Patricia Chiril, Farah Benamara, Véronique Moriceau. Topic Refinement in Multi-level Hate Speech Detection. 45th European Conference on Information Retrieval (ECIR 2023), Apr 2023, Dublin, Ireland. pp.367–376, ⟨10.1007/978-3-031-28238-6_26⟩. ⟨hal-04059746⟩Google Scholar
- Wu, Zhihao & Zhang, Baopeng & Zhou, Tianchen & Li, Yan & Fan, Jianping. (2021). Automatic Detection of Discrimination Actions from Social Images. Electronics. 10. 325. 10.3390/electronics10030325.Google Scholar
- Balandina, N., Pankevych, O., Liubarets, V., Vyshnevska, Y., & Rodinova, N. (2023). Gerontological ageism as a social challenge and its detection in Ukrainian news media content. Revista Latina de Comunicación Social, 81, 133-154. https://www.doi.org/10.4185/RLCS-2023-1828Google ScholarCross Ref
- https://longreads.cabar.asia/gender_stereotypesGoogle Scholar
- https://kazlenta.kz/15061-kazhdyy-pyatyy-kazahstanskiy-podrostok-stanovitsya-zhertvoy-ili-uchastnikom-bullinga.htmlGoogle Scholar
- Anna Pillar, Kyrill Poelmans, and Martha Larson. 2022. Regex in a Time of Deep Learning: The Role of an Old Technology in Age Discrimination Detection in Job Advertisements. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 13–18, Dublin, Ireland. Association for Computational Linguistics.Google ScholarCross Ref
- Chu CH, Leslie K, Shi J, Nyrup R, Bianchi A, Khan SS, Rahimi SA, Lyn A, Grenier A Ageism and Artificial Intelligence: Protocol for a Scoping Review JMIR Res Protoc 2022;11(6):e33211Google Scholar
- Soto-Perez-de-Celis, Enrique. (2020). Social media, ageism, and older adults during the COVID-19 pandemic. EClinicalMedicine. 29-30. 100634. 10.1016/j.eclinm.2020.100634.Google Scholar
- Sreekanth Madisetty and Maunendra Sankar Desarkar. 2018. Aggression Detection in Social Media using Deep Neural Networks. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 120–127, Santa Fe, New Mexico, USA. Association for Computational Linguistics.Google Scholar
- Investigating Deep Learning Approaches for Hate Speech Detection in Social Media, Prashant Kapil, Asif Ekbal, Dipankar Das, arXiv:2005.14690 [cs.CL], (or arXiv:2005.14690v1 [cs.CL] for this version), https://doi.org/10.48550/arXiv.2005.14690Google ScholarCross Ref
- https://developers.google.com/youtube/v3?hl=ruGoogle Scholar
- Burkov А. Machine learning without unnecessary words. – Saint-Petersburg: Piter, 2020. –P.192Google Scholar
Index Terms
- Identifying Cyber-Threatening Texts in the Kazakh Segment of Web Resources
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