Abstract
AbstractThe growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.
Funder
NTNU Norwegian University of Science and Technology
Publisher
Springer Science and Business Media LLC
Reference76 articles.
1. Akuma S, Lubem T, Adom IT (2022) Comparing bag of words and tf-idf with different models for hate speech detection from live tweets. Int J Inform Technol 14(7):3629–3635
2. Ali R, Farooq U, Arshad U et al (2022) Hate speech detection on twitter using transfer learning. Comput Speech Lang 74:101365
3. Andreassen SM, Seim GT (2020) Detecting and grading hateful messages in the norwegian language. Master’s thesis, NTNU
4. Aswad E (2016) The role of us technology companies as enforcers of Europe’s new internet hate speech ban. HRLR Online 1:1
5. Awal MR, Lee RKW, Tanwar E, et al (2023) Model-agnostic meta-learning for multilingual hate speech detection. IEEE Trans Comput Soc Syst
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献