Print  Close

Is your paper going to be cited? A Multinomial Inverse Regression model for predicting citations.

Tarifa Almulhim Co-Author
King Faisal University. Business School. Department of Quantitative Methods. Business
 
Igor Barahona First Author
King Fahad University of Petroleum and Minerals
 
Igor Barahona Presenting Author
King Fahad University of Petroleum and Minerals
 
Monday, Aug 5: 11:35 AM - 11:50 AM
2943 
Contributed Papers 
Oregon Convention Center 
This study draws upon the complete text of a collection comprising 751 scientific articles. These articles specifically feature the terms 'renewable energies' and 'circular economies' either in their titles or abstracts. Then a novel application of the Multinomial Inverse Regression to predict the number of citations is investigated. This prediction is based on textual data, coupled with a set of related covariates. For the proposed model, measures of goodness of fit, as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are provided. By investigating characteristic words and related covariates that are associated with a higher number of citations, this work aims to provide significant evidence for researchers and practitioners..

Keywords

Predictive Model

Textual Data

Renewable Energies

Circular Economies

Multinomial Inverse Regression. 

Main Sponsor

Section on Text Analysis