对比多种模型在社交媒体欺凌检测中的表现，BERT综合最优。
Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms
- 用BERT等大模型对比检测网络欺凌，聚焦实际性能与资源消耗。
- BERT准确率、召回率、F1均达95%，推理仅需0.053秒，能耗极低。
- 生成式模型不总胜出，精细调优现有模型更高效实用。
网络欺凌对公众心理健康造成严重负面影响，是社交媒体平台上的普遍问题，亟需高效、实时的检测与监控系统。然而，现有检测系统在性能、数据质量、时间效率和计算成本方面仍面临挑战。本研究通过适配与评估现有文本分类技术，在网络欺凌检测领域开展对比实验，重点考察BERT、RoBERTa、XLNet、DistilBERT和GPT-2.0等大语言模型的适用性。结果表明，BERT在性能、效率与资源消耗间取得最佳平衡：准确率95%、精确率95%、召回率95%、F1分数95%、错误率5%、推理时间0.053秒、内存占用35.28 MB、CPU/GPU使用率0.4%、能耗0.000263 kWh。研究显示，生成式AI模型虽强大，但在测试基准上并未持续优于微调模型；通过针对性调优现有模型，仍可实现顶尖性能。
原文摘要 · Abstract (English)
Cyberbullying significantly contributes to mental health issues in communities by negatively impacting the psychology of victims. It is a prevalent problem on social media platforms, necessitating effective, real-time detection and monitoring systems to identify harmful messages. However, current cyberbullying detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This research aims to conduct a comparative study by adapting and evaluating existing text classification techniques within the cyberbullying detection domain. The study specifically evaluates the effectiveness and performance of these techniques in identifying cyberbullying instances on social media platforms. It focuses on leveraging and assessing large language models, including BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0, for their suitability in this domain. The results show that BERT strikes a balance between performance, time efficiency, and computational resources: Accuracy of 95%, Precision of 95%, Recall of 95%, F1 Score of 95%, Error Rate of 5%, Inference Time of 0.053 seconds, RAM Usage of 35.28 MB, CPU/GPU Usage of 0.4%, and Energy Consumption of 0.000263 kWh. The findings demonstrate that generative AI models, while powerful, do not consistently outperform fine-tuned models on the tested benchmarks. However, state-of-the-art performance can still be achieved through strategic adaptation and fine-tuning of existing models for specific datasets and tasks.
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