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- W4312706762 abstract "AbstractOver the last decade, researchers presented (semi-)automated comment moderation systems (CMS) based on machine learning (ML) and natural language processing (NLP) techniques to support the identification of hateful and offensive comments in online discussion forums. A common challenge in providing and operating comment moderation systems is the dynamic nature of language. As language evolves over time, continuous performance evaluations and resource-inefficient model retraining are applied to ensure high-quality identification of hate speech in the long-term use of comment moderation systems. To study the potentials of adaptable machine learning models embedded in comment moderation systems, we present an incremental machine learning approach for semi-automated comment moderation systems. This study shows a comparison of incrementally-trained ML models and batch-trained ML models used in comment moderation systems.KeywordsIncremental learningText classificationComment moderation systems" @default.
- W4312706762 created "2023-01-05" @default.
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- W4312706762 date "2022-01-01" @default.
- W4312706762 modified "2023-10-16" @default.
- W4312706762 title "Incremental Machine Learning for Text Classification in Comment Moderation Systems" @default.
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- W4312706762 doi "https://doi.org/10.1007/978-3-031-18253-2_10" @default.
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