Mul-GAD vs. Shallow Learning Methods in Text Classification Benchmark Performance

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does the performance of Mul-GAD compare to traditional shallow learning methods (e.g., SVM, Random Forest) on benchmark text classification datasets like 20 Newsgroups or Reuters when evaluated. Abstract The rapid proliferation of hate speech on social media poses significant challenges to maintaining a safe and inclusive digital environment. This paper presents a comprehensive review of automatic hate speech detection methods, with a particular focus on the evolution. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the performance of Mul-GAD compare to traditional shallow learning methods (e.g., SVM, Random Forest) on benchmark text classification datasets like 20 Newsgroups or Reuters when evaluated using F1-score and AUC-ROC?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance