Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913628648636416 |
|---|---|
| author | Philipo, Adamu Gaston Sarwatt, Doreen Sebastian Ding, Jianguo Daneshmand, Mahmoud Ning, Huansheng |
| author_facet | Philipo, Adamu Gaston Sarwatt, Doreen Sebastian Ding, Jianguo Daneshmand, Mahmoud Ning, Huansheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms Philipo, Adamu Gaston Sarwatt, Doreen Sebastian Ding, Jianguo Daneshmand, Mahmoud Ning, Huansheng Computation and Language Social and Information Networks 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. |
| title | Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms |
| topic | Computation and Language Social and Information Networks |
| url | https://arxiv.org/abs/2412.19928 |