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An Automated System to Predict Popular Cybersecurity News Using Document Embeddings
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...
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Published in: | Computer modeling in engineering & sciences 2021-01, Vol.127 (2), p.533-547 |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 1526-1492 1526-1506 1526-1506 |
DOI: | 10.32604/cmes.2021.014355 |