Improving trend analysis using social network features

Abstract

Abstract In recent years, large volumes of data have been massively studied by researchers and organizations. In this context, trend analysis is one of the most important areas. Typically, good prediction results are hard to obtain because of unknown variables that could explain the behaviors of the subject of the problem. This paper goes beyond standard trend identification methods that consider only historical behavior of the objects by including the structure of the information sources, i.e., social network metrics, as an additional dimension to model and predict trends over time. Results from a set of experiments indicate that including such metrics has improved the prediction accuracy. Our experiments considered the publication titles, as recorded in the Brazilian Lattes database, from all the Ph.Ds. in Computer Science registered in the Brazilian Lattes platform for the periods analyzed in order to evaluate the proposed trend prediction approach.