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Culture Convergence (CC)
A Practical Deep Learning-Based Approach for the Classification of Hate Speech and Offensive Language
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- 발행기관
- 국제문화기술진흥원 바로가기
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- 간행물
- International Journal of Advanced Culture Technology(IJACT) KCI 등재 바로가기
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- 통권
- Volume 13 Number 4 (2025.12)바로가기
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- 페이지
- pp.1-7
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A481093
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The proliferation of social media has facilitated communication but has also accelerated the spread of hate speech and offensive language. The automatic detection of such harmful content is essential for ensuring the safety and integrity of online platforms. This study proposes a practical classification system for hate speech detection using Google’s BERT (Bidirectional Encoder Representations from Transformers) model. A dataset consisting of 24,783 tweets was utilized, categorized into three labels: Hate Speech, Offensive Language, and Neither. To address the issue of data imbalance, techniques such as SMOTE, class weighting, and threshold optimization were applied. The preprocessing pipeline included the removal of URLs, user tags, and special characters to enhance data consistency. Compared with traditional machine learning models—Logistic Regression, Random Forest, and SVM—the BERT-based model demonstrated superior performance in both contextual understanding and classification accuracy. Model performance was evaluated using confusion matrices, precision, recall, F1-score, as well as ROC and Precision-Recall curves, showing particularly strong results for the Offensive Language category. Future research will extend this work by applying advanced transformer-based models such as RoBERTa and GPT-3, and by constructing multilingual datasets to develop a scalable, real-time detection system applicable to global social media platforms.
목차
Abstract
1. INTRODUCTION
2. Main Body
2.1 Related Work
2.2 Dataset and Data Imbalance Problem
3. Proposed Method
3.1 Model Overview
3.2 Training Configuration
3.3 Training and Validation Strategy
4. Results
4.1 Comparison with Traditional Machine Learning Models
4.2 Performance Evaluation and Visualization
5. Conclusion and Future Work
REFERENCES
키워드
Hate Speech Detection Offensive Language BERT Deep Learning Data Imbalance Natural Language Processing (NLP)저자
간행물 정보
발행기관
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- 발행기관명
- 국제문화기술진흥원 [The International Promotion Agency of Culture Technology]
- 설립연도
- 2009
- 분야
- 공학>공학일반
- 소개
- 본 진흥원은 문화기술(Culture Technology) 관련 산·학·연·관으로 구성된 비영리 단체이다. 문화기술(CT)은 정보통신기술(ICT), 문화적 사고 기반의 예술, 인문학, 디자인, 사회과학기술이 접목된 신융합기술(New Convergence Technology, NCT)로 정의한다. 인간의 삶의 질을 향상시키고, 진보된 방향으로 변화시키고, 문화기술 관련 분야의 학술 및 기술의 발전과 진흥에 공헌하기 위하여, 제3조의 필요한 사업을 행함을 그 목적으로 한다.
간행물
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- 간행물명
- International Journal of Advanced Culture Technology(IJACT)
- 간기
- 계간
- pISSN
- 2288-7202
- eISSN
- 2288-7318
- 수록기간
- 2013~2025
- 등재여부
- KCI 등재
- 십진분류
- KDC 600 DDC 700
이 권호 내 다른 논문 / International Journal of Advanced Culture Technology(IJACT) Volume 13 Number 4
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