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An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

Ramsha Saeed1, Saddaf Rubab1, Sara Asif1, Malik M. Khan1, Saeed Murtaza1, Seifedine Kadry2, Yunyoung Nam3,*, Muhammad Attique Khan4,*

1 National University of Sciences and Technology, Islamabad, Pakistan
2 Beriut Arab University, Beirut, Lebanon
3 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
4 Hitec University, Taxila, Pakistan

* Corresponding Authors: Yunyoung Nam. Email: ; Muhammad Attique Khan. Email:

Computer Modeling in Engineering & Sciences 2021, 127(2), 533-547.


The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically enriched model is proposed which estimates news popularity by measuring cosine similarity between document embeddings of the news articles. Word2vec model has been used to generate distributed representations of the news content. In this work, we define popularity as the number of times a news article is posted on different websites. We collect data from different websites that post news concerning the domain of cybersecurity and estimate the popularity of cybersecurity news. The proposed approach is compared with different models and it is shown that it outperforms the other models.


Cite This Article

Saeed, R., Rubab, S., Asif, S., Khan, M. M., Murtaza, S. et al. (2021). An Automated System to Predict Popular Cybersecurity News Using Document Embeddings. CMES-Computer Modeling in Engineering & Sciences, 127(2), 533–547.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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