A proactive intelligent decision support system for predicting the popularity of online news
| Data(s) |
01/09/2015
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| Resumo |
"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273" Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors. This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The authors would like to thank Pedro Sernadela for his contributions in previous work |
| Identificador |
In F. Pereira, P. Machado, E. Costa and A. Cardoso (Eds.), 17th Portuguese Conference on Artificial Intelligence (EPIA 2015), Lecture Notes in Artificial Intelligence 9273, pp. 535-546, Coimbra, Portugal, September, 2015, Springer, ISBN 978-3-319-23484-7. 978-3-319-23484-7 978-3-319-23485-4 0302-9743 http://hdl.handle.net/1822/39169 10.1007/978-3-319-23485-4_53 |
| Idioma(s) |
eng |
| Relação |
info:eu-repo/grantAgreement/FCT/5876/147280/PT http://link.springer.com/chapter/10.1007%2F978-3-319-23485-4_53 |
| Direitos |
info:eu-repo/semantics/openAccess |
| Palavras-Chave | #Popularity Prediction #Online News #Text Mining #Classification #Stochastic Local Search #Data Mining #Decision Support System |
| Tipo |
info:eu-repo/semantics/conferenceObject |
