Author(s)

Jorge Arroba Rimassa¹, Rafael Muñoz Guillena², Fernando Llopis²

¹Facultad de Ingeniería Ciencias Físicas y Matemáticas, Universidad Central del Ecuador, Quito, Ecuador.
²Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, España.

The result of the analysis of a thematic in a social network is to find a measure that allows the principal actors to know their performance, that is, they can define or maintain strategies and courses of action in order to optimize their communication. It is necessary to formally define the principles of analysis in Social Networks in order to use their characteristics better and to be able to contextualize the concept and use of weighting factors to improve their predictability. When Social Networks are going to be used as a mechanism to predict social behavior, for example, to predict the outcome of a political election, weighting factors must be introduced to try to match the data collected from the Social Network with those of a sample. In this article we have defined the methodology to incorporate the geographic weighting factors and several formulas have been created that allow reprocessing the data downloaded from Twitter in which its polarity has been determined by classical NLP methods to increase the predictive power.

Twitter, Weighting Factors, Formalization of Social Networks

Rimassa, J. , Guillena, R. and Llopis, F. (2018) The Weighting Factors to Improve Predictability on Twitter. Technology and Investment, 9, 68-79. doi: 10.4236/ti.2018.91005.