인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes classifiers in identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained and tested on a dataset curated for the detection of cyber trolls. The performance of the classifiers was gauged using confusion matrices, which provide detailed counts of true positives, true negatives, false positives, and false negatives. These metrics were then utilized to calculate the accuracy, precision, recall, and F1 scores to better understand each model’s predictive capabilities. The Random Forest classifier outperformed other models, exhibiting the highest accuracy and balanced precision-recall trade-off, as indicated by the highest true positive and true negative rates, alongside the lowest false positive and false negative rates. LightGBM, while effective, showed a tendency towards higher false predictions. Logistic Regression, SVM, and Naive Bayes displayed identical confusion matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. The findings underscore the effectiveness of ensemble methods, with Random Forest leading in the cyber troll detection task. The study highlights the importance of selecting appropriate ML algorithms for text classification tasks in social media contexts and emphasizes the need for further scrutiny into the anomaly observed among the Logistic Regression, SVM, and Naive Bayes results. Future work will focus on exploring the reasons behind this occurrence and the potential of deep learning techniques in enhancing detection performance.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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