Adölesan Ruh Sağlığında Dijital Riskler ve Yapay Zekâ Destekli Yanıtlar: Bibliyometrik ve Bilim Haritalama Analizi

Öz

Amaç: Bu çalışma, ergen ruh sağlığındaki dijital riskleri ve yapay zekâ destekli yanıtları haritalandırmış, yayın dengesini nicelleştirmiş ve her iki alanın aynı yayınlar içinde ne sıklıkta açıkça entegre edildiğini test etmiştir. Yöntem: Scopus veri tabanına ait kayıtlar (9 Ağustos 2026'da dondurulan; 15 Ağustos 2026'da dışa aktarılan metadalar), makale, derleme ve bildiri türlerini kapsayacak şekilde bibliyometrik haritalama ile incelenmiştir. Zamansal eğilimler 2015–2025 (tam yıllar) dönemi için değerlendirilmiş, 2026 yılı ise geçici olarak raporlanmıştır. Kayıtlar Sadece Risk, Sadece Yanıt veya Her İkisi şeklinde sınıflandırılmıştır. Dayanıklılık kontrolleri; denetimleri, Her İkisi kategorisindeki tüm kayıtların manuel doğrulanmasını, duyarlılık analizlerini, VOSviewer haritalamasını, tematik gelişimi, patlama tespitini ve ağ doğrulamasını içermiştir. Bulgular: Elde edilen 4.804 kayıttan 1.623'ü birincil korposu oluşturmuştur (2015–2025 döneminde 1.335 kayıt: 833 Sadece Risk, 495 Sadece Yanıt, 7 Her İkisi). Yanıt/risk oranı 2015'teki 0,04 değerinden, yanıt üretiminin ilk kez riski geçtiği 2025 yılında 1,19'a yükselmiştir. Manuel inceleme, 2015–2025 korposunda yalnızca 5 (%0,37) ve tüm kesitte toplam 9 (%0,55) doğrudan köprü bulunduğunu doğrulamış; entegrasyon oranı duyarlılık analizlerinde %1'in altında kalmıştır. Anahtar kelime haritalaması; siber zorbalık/mağduriyet, YZ/dijital-ruh-sağlığı ve makine-öğrenimi/intihar-tahmini alanlarının belirgin ayrımını, son dönemde ise YZ, dijital fenotipleme ve büyük dil modellerindeki büyümeyi göstermiştir. Sonuç: Yapay zekâ destekli yanıt araştırmaları hızla büyümüş olsa da, dijital risk ile yapay zekâ yanıtının aynı yayın içinde açıkça bir arada ele alınması nadir görülmekte ve 2026 geçici verilerine dayanmamaktadır.

Anahtar Kelimeler

• Adölesan
• Ruh sağlığı
• Yapay zeka
• Makine Öğrenmesi
• Siber Zorbalık

Digital Risks and AI-Enabled Responses in Adolescent Mental Health: A Bibliometric and Science-Mapping Analysis

Öz

Objective: This study mapped digital risks and AI-enabled responses in adolescent mental health, quantified publication balance, and tested explicit domain integration within single records. Method: Scopus metadata (frozen August 9, 2026; exported August 15, 2026) covering articles, reviews, and conference papers were analyzed via bibliometric mapping. Temporal trends evaluated 2015–2025 (complete years), reporting 2026 provisionally. Records were classified as Risk-only, Response-only, or Both. Robustness checks included audits, manual validation of Both records, sensitivity analyses, VOSviewer mapping, thematic evolution, burst detection, and network validation. Results: Of 4,804 retrieved records, 1,623 formed the primary corpus (1,335 in 2015–2025: 833 Risk-only, 495 Response-only, 7 Both). The response-to-risk ratio rose from 0.04 (2015) to 1.19 (2025), when response output first surpassed risk. Manual review confirmed only 5 direct bridges in 2015–2025 (0.37%) and 9 overall (0.55%); integration remained <1% across sensitivity analyses. Keyword mapping revealed distinct cyberbullying/victimization, AI/digital-mental-health, and machine-learning/suicide-prediction clusters, with recent growth in AI, digital phenotyping, and LLMs. Conclusion: Although AI response research has expanded rapidly, explicit co-framing of digital risk and AI response within single publications remains rare and independent of 2026 data.

Anahtar Kelimeler

• Adolescent
• Mental Health
• Artificial Intelligence
• Machine Learning
• Cyberbullying

Kaynakça

1. Aboujaoude E, Savage MW, Starcevic V, Salame WO (2015) Cyberbullying: Review of an old problem gone viral. J Adolesc Health, 57:10-18.
2. Armitage R (2021) Bullying in children: Impact on child health. BMJ Paediatr Open, 5:e000939.
3. Baas J, Schotten M, Plume AM, Côté G, Karimi R (2020) Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quant Sci Stud, 1:377-386.
4. Babar A, Asif MS, Kharub I, Beydoun G (2026) Cyberbullying among adolescents: Mapping 25 years of global research. J Manag Organ, 32:1145-1167.
5. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp, 2008:P10008.
6. Boer M, Stevens GWJM, Finkenauer C, de Looze ME, van den Eijnden RJJM (2021) Social media use intensity, social media use problems, and mental health among adolescents: Investigating directionality and mediating processes. Comput Human Behav, 116:106645.
7. Bottino SMB, Bottino CMC, Regina CG, Correia AVL, Ribeiro WS (2015) Cyberbullying and adolescent mental health: Systematic review. Cad Saude Publica, 31:463-475.
8. Clark A (2025) The ability of AI therapy bots to set limits with distressed adolescents: Simulation-based comparison study. JMIR Ment Health, 12:e78414.

9. Dalouh Ounia R, Soriano Ayala E, Caballero Cala V (2025) Deepfakes and digital violence in adolescent couples: Perceptions, risks, and prevention strategies. Pedagogika, 160:74-93.
10. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res, 133:285-296.
11. Dosovitsky G, Bunge E (2023) Development of a chatbot for depression: Adolescent perceptions and recommendations. Child Adolesc Ment Health, 28:124-127.
12. Feng Y, Hang Y, Wu W, Song X, Xiao X, Dong F et al. (2025) Effectiveness of AI-driven conversational agents in improving mental health among young people: Systematic review and meta-analysis. J Med Internet Res, 27:e69639.
13. Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P et al. (2015) Prevalence and effect of cyberbullying on children and young people: A scoping review of social media studies. JAMA Pediatr, 169:770-777.
14. John A, Glendenning AC, Marchant A, Montgomery P, Stewart A, Wood S et al. (2018) Self-harm, suicidal behaviours, and cyberbullying in children and young people: Systematic review. J Med Internet Res, 20:e129.
15. Kadirvelu B, Bellido Bel T, Freccero A, Di Simplicio M, Nicholls D, Faisal AA (2026) Digital phenotyping for adolescent mental health: Feasibility study using machine learning to predict mental health risk from active and passive smartphone data. J Med Internet Res, 28:e72501.
16. Kelly Y, Zilanawala A, Booker C, Sacker A (2018) Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study. EClinicalMedicine, 6:59-68.
17. Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining:91-101. July 23-26, 2002, Edmonton, Alberta, Canada. New York, Association for Computing Machinery.
18. Montazeri A, Mohammadi S, Mokhtari-Hesari P, Ghaemi M, Riazi H, Sheikhi-Mobarakeh Z (2023) Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): A minimum requirements. Syst Rev, 12:239.
19. O’Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P (2018) Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry, 23:601-613.
20. Pellegrino A, Stasi A (2024) A bibliometric analysis of the impact of media manipulation on adolescent mental health: Policy recommendations for algorithmic transparency. Online J Commun Media Technol, 14:e202453.
21. Radovic A, Gmelin T, Stein BD, Miller E (2017) Depressed adolescents’ positive and negative use of social media. J Adolesc, 55:5-15.
22. Sharma G, Yaffe MJ, Ghadiri P, Gandhi R, Pinkham L, Gore G et al. (2025) Use of artificial intelligence in adolescents’ mental health care: Systematic scoping review of current applications and future directions. JMIR Ment Health, 12:e70438.
23. Singh VK, Singh P, Karmakar M, Leta J, Mayr P (2021) The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126:5113-5142.
24. Sobowale K, Humphrey DK, Zhao SY (2025) Evaluating generative AI psychotherapy chatbots used by youth: Cross-sectional study. JMIR Ment Health, 12:e79838.
25. Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-Jepsen M, Whelan P et al. (2021) The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20:318-335.
26. van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84:523-538.
27. Viner RM, Aswathikutty-Gireesh A, Stiglic N, Hudson LD, Goddings AL, Ward JL et al. (2019) Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: A secondary analysis of longitudinal data. Lancet Child Adolesc Health, 3:685-696.
28. Wang X, Zhou Y, Zhou G (2025) The application and ethical implication of generative AI in mental health: Systematic review. JMIR Ment Health, 12:e70610.
29. WHO (2025) Mental health of adolescents. https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health (Accessed 15.08.2026).
30. World Health Organization Regional Office for Europe (2025) Addressing the Digital Determinants of Youth Mental Health and Well-Being: Policy Brief. Copenhagen, World Health Organization Regional Office for Europe.