An Integrated Framework for Relevance Classification of Trending Topics in Arabic Tweets

Social media platforms such as Twitter are a valuable source of information about current events and trends. Trending topics aim to promote public events such as political events, market changes, and other types of breaking news. However, with so much data being generated, it would be difficult to identify relevant tweets that are related to a particular trending topic. Therefore, in this paper, an integrated framework is proposed for the detection of the degree of relevance between Arabic tweets and trending topics. This framework integrates natural language processing, data augmentation, and machine learning techniques to identify text that is likely to be relevant to a given trending topic. The proposed framework was evaluated using a real-life dataset of Arabic tweets that was collected and labeled. The results of the evaluation showed that the proposed framework achieved the highest macro F1 score of 82% in binary classification (relevant/irrelevant) and 77% in categorical classification (degree of relevance), which outperforms the current state of the art.

@article{Alkadri2023,
  title     = {An Integrated Framework for Relevance Classification of Trending Topics in Arabic Tweets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Abdullah M. Alkadri and Abeer ElKorany and Cherry A. Ezzat},
  doi       = {10.14569/IJACSA.2023.0140796},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140796}
}