Federated-Learning Topic Modeling Based Text Classification Regarding Hate Speech During COVID-19 Pandemic

One of the most challenging tasks in knowledge discovery is extracting the semantics of the content regarding emotional context from the natural language text. The COVID-19 pandemic gave rise to many serious concerns and has led to several controversies including spreading of false news and hate speech. This paper particularly focuses on Islamophobia during the COVID-19. The widespread usage of social media platforms during the pandemic for spreading of false information about Muslims and their common religious practices has further fueled the existing problem of Islamophobia. In this respect, it becomes very important to distinguish between the genuine information and the Islamophobia related false information. Accordingly, the proposed technique in this paper extracts features from the textual content using approaches like Word2Vec and Global Vectors. Next, the text classification is performed using various machine learning and deep learning techniques. The performance comparison of various algorithms has also been reported. After experimental evaluation, it was found that the performance metric like F1-score indicate that Support Vector Machine performs better than other alternatives. Similarly, Convolutional Neural Network also achieved promising results.

@article{Kamran2023,
  title     = {Federated-Learning Topic Modeling Based Text Classification Regarding Hate Speech During COVID-19 Pandemic},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Kamran and Ammar Saeed and Ahmed Almaghthawi},
  doi       = {10.14569/IJACSA.2023.0141157},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141157}
}