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

Preiss, J ORCID: https://orcid.org/0000-0002-2158-5832 2022, 'Predicting the impact of online news articles – is information necessary? : application to COVID-19 articles' , Multimedia Tools and Applications .

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview

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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Multimedia Tools and Applications
Publisher: Springer
ISSN: 1380-7501
Related URLs:
Funders: Greater Manchester AI Foundry project
Depositing User: USIR Admin
Date Deposited: 10 Jan 2022 15:33
Last Modified: 18 Feb 2022 09:45
URI: http://usir.salford.ac.uk/id/eprint/62769

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year