Detecting Offensive Posts on Social Media
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2024, 00, pp. 1-6
- Issue Date:
- 2024-01-22
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Detecting_Offensive_Posts_on_Social_Media.pdf | Published version | 313.18 kB |
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While social media platforms bring people closer together and facilitate the rapid spread of information they also face the challenge of offensive and harmful content such as bullying discrimination and hate speech Most recent research has focused on detecting text based offensive language in social media However audio based detection methods are still underdeveloped This paper addresses this gap by proposing a novel deep learning based model CLS CNN for identifying objectionable audio We collect a video dataset for training machine learning models for detecting offensive language We compare the performance of CLS CNN with traditional machine learning methods using the collected dataset Results showed that the CLS CNN model achieved an accuracy of 88 on our collected dataset and outperformed other models The model was also evaluated using three publicly available text based datasets It shows that CLS CNN achieves at least a 2 accuracy improvement compared to the other published schemes using the same dataset
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