Predicting the impact of online news articles – is information necessary? : application to COVID-19 articles

Preiss, J

Abstract

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features – ranging from the semantic relations through readability measures to the article’s digital content – are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

Citation

Preiss, J. (2022). Predicting the impact of online news articles – is information necessary? : application to COVID-19 articles. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-021-11621-5

• Journal Article Type: Article
• Acceptance Date: Sep 23, 2021
• Online Publication Date: Jan 8, 2022
• Publication Date: Jan 8, 2022
• Deposit Date: Jan 10, 2022
• Publicly Available Date: Jan 10, 2022
• Journal: Multimedia Tools and Applications
• Print ISSN: 1380-7501
• Electronic ISSN: 1573-7721
• Publisher: Springer Verlag
• DOI: https://doi.org/10.1007/s11042-021-11621-5
• Publisher URL: https://doi.org/10.1007/s11042-021-11621-5
• Related Public URLs: http://link.springer.com/journal/11042