Detection Cyberbullying Using AI and Sentiment Analysis to Examine Psychological Impacts on Vulnerable Groups
Yazarlar (4)
Abdulnaser Mutashar Fashakh
Dr. Öğr. Üyesi Mesut Çevik Türkiye
Öğr. Gör. Şenay KOCAKOYUN AYDOĞAN Sinop Üniversitesi, Türkiye
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Egyptian Informatics Journal (Q2) | ||
| Dergi ISSN | 1110-8665 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SSCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Cilt / Sayı / Sayfa | 32 / 0 / 1–15 | DOI | 10.1016/j.eij.2025.100856 |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S111086652500249X | ||
| UAK Araştırma Alanları |
Yapay Zeka
Bilgi Sistemleri
Veri Madenciliği
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| Özet |
| This study aims to assess the effectiveness of machine learning and deep learning models in detecting cyberbullying and evaluating its psychological impact on vulnerable groups using textual and emotional features. The models assessed include traditional classifiers—Logistic Regression, Decision Tree, and Random Forest and deep learning models, such as MLP, CNN, RNN, and (LSTM) networks. TF-IDF for text vectorization and TextBlob for sentiment analysis were utilized. In spite of TF-IDF's shortcoming. Its simplicity enabled quick prototyping and insight results. The dataset contained 58,000 tweets, with 46,000 obtained from Kaggle and 12,000 collected via the Twitter API. Tweets were labeled into cyberbullying_type (gender, age, religion, and ethnicity) and subcategories: gender (male, female, LGBT, other), age (adult, teenager, other), religion (Muslim, Christian, Jewish, other), and ethnicity (ethical … |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 5 |