Incorporating Expert Judgment for Detecting Relevant Factors in Social Networks Undetected by Ordinary Methods
DOI:
https://doi.org/10.46661/rev.metodoscuant.econ.empresa.8135Keywords:
Bayesian inference, MCMC simulation methods, informative prior distributions, social networksAbstract
Information and communications technology (ICT) has potential to complement
information sharing bureaus (ISB) Most companies use social networks as
communication channels because they can provide significant business
benefits. This paper focuses on the impact of social networks in a Spanish
foundation for innovation and knowledge dissemination, and how they affect its
main events and activities. We examine the factors underlying a re-tweet on
Twitter or a share on Facebook in order to analyze reporting of this foundation’s
principal events. Comparisons with three statistical models were performed
(standard regression and Bayesian regression with non-informative and
informative priors). We conclude that the advantage offered by Bayesian over
classic methodology is demonstrated by incorporation of collateral information,
usually provided by experts, which can refine the model and obtain conclusions
that cannot be identified otherwise. This conclusion may have significant
implications for companies that make use of social networks
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