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

Abstract Number:

2943

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Paper

Participants:

Igor Barahona (1), Tarifa Almulhim (2)

Institutions:

(1) King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia, (2) King Faisal University. Business School. Department of Quantitative Methods. Business, Al-Hasa, Saudi Arabia

Co-Author:

Tarifa Almulhim
King Faisal University. Business School. Department of Quantitative Methods. Business

First Author:

Igor Barahona
King Fahad University of Petroleum and Minerals

Presenting Author:

Igor Barahona
King Fahad University of Petroleum and Minerals

Abstract Text:

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:

Sponsors:

Section on Text Analysis

Tracks:

Miscellaneous

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