Abstract
This paper aims to determine and estimate an econometric model which can be used to forecast media popularity of a governmental organization. Number of media sources monitored was used as regressors while taking types of these sources into account. Some linear models were estimated besides some non-linear models. According to the results, number of national, local, regional newspapers and number of television channels monitored were not found important to estimate number of news caught through media monitoring. On the other hand, number of internet media sources was found important to estimate the dependent variable. Additionally, number of news caught on select subjects in previous year was also found important. In the end an autoregressive panel data model with some additional regressors such as number of monitored sources was suggested to forecast popularity of organization. Any data only accessible to TurkStat members was never used in this paper. TurkStat is not responsible for any inference made in this study.
Keywords
References
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Abstract
References
- [1] Craufurd Smith R., Monitoring media pluralism in the digital era: application of the Media Pluralism Monitor 2020 in the European Union, Albania and Turkey in the years 2018-2019. Country report: United Kingdom , (2020).
- [2] Mukhamediev R. I., Yakunin K., Mussabayev R., Buldybayev T., Kuchin Y., Murzakhmetov S., Yelis M. (2020). Classification of Negative Information on Socially Significant Topics in Mass Media, Symmetry, 12(12) (2020) 1945.
- [3] Sánchez-Núñez P., Yanez E. R., Cabrera F. E., Peláez-Repiso A., Government Communication Management in Digital Ecosystems: A Real Case of Country Brand Analysis, In 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 264-268) (2020, April) IEEE.
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- [13] Trattner C., Moesslang D., Elsweiler D.,. On the predictability of the popularity of online recipes. EPJ Data Science, 7(1) (2018) 1-39.
Details
| Primary Language | English |
|---|---|
| Subjects | Statistics |
| Journal Section | Natural Sciences |
| Authors | |
| Publication Date | December 29, 2021 |
| Submission Date | July 26, 2021 |
| Acceptance Date | November 21, 2021 |
| Published in Issue | Year 2021Volume: 42 Issue: 4 |