UTILIZATION OF ADVANCED MACHINE LEARNING TECHNIQUES FOR DETECTING HATE SPEECH ON SOCIAL MEDIA PLATFORMS / (Kayıt no. 293069)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02608nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20250110111010.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy ed||| |||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3578 |
| Cutter no | N46 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Ngoy, Isaac Mumba |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | UTILIZATION OF ADVANCED MACHINE LEARNING TECHNIQUES FOR DETECTING HATE SPEECH ON SOCIAL MEDIA PLATFORMS / |
| Sorumluluk Bildirimi | ISAAC MUMBA NGOY ; SUPERVISOR, ASST. PROF. DR. KIAN JAZAYERI |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 64 sheets ; |
| Boyutları | 30 cm |
| Birlikteki Materyal | +1 CD ROM |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information System |
| 520 ## - ÖZET NOTU | |
| Özet notu | This study examines the efficacy of diverse machine learning models in detecting hate speech within English-language tweets, with a focus on advanced ensemble methods. The study evaluates a range of models, including Random Forest, Stacking Classifier, Support Vector Machine (SVM), Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and Gradient Boosting. Random Forest emerged as the top performer, achieving an accuracy of 99.90%, precision of 99.94%, recall of 99.87%, F1-score of 99.90%, and an AUC-ROC of 0.999, closely followed by the Stacking Classifier and SVM. A key contribution of this research lies in its emphasis on preprocessing techniques, particularly the use of lemmatization and contraction expansion, which have been less commonly applied in the field compared to stemming. These techniques, along with text cleaning, normalization, and tokenization, were crucial in improving the models' accuracy and ability to capture the nuances of hate speech. Feature extraction was conducted using the Term Frequency-Inverse Document Frequency (TF-IDF), further augmenting the models' ability to differentiate between hate speech and non-hate speech content. The study highlights the significance of sophisticated preprocessing in increasing the robustness of machine learning models for hate speech detection. This research delivers critical insights that can enhance the effectiveness of hate speech detection systems on social media platforms and establishes a foundation for future studies focused on advanced deep learning approaches and the ethical aspects of deploying these models. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Management Information System |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Jazayeri, Kian |
| İlişkili Terim | supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 19.11.2024 | Bağış | YL 3578 N46 2024 | T4025 | 19.11.2024 | C.1 | 19.11.2024 | Thesis | Management Information System | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 19.11.2024 | Bağış | YL 3578 N46 2024 | CDT4025 | 19.11.2024 | C.1 | 19.11.2024 | Suppl. CD | Management Information System |