Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario - Santos, Jéssica S.; Bernardini, Flávia; Paes, Aline (Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM); 2021: Anais do X Brazilian Workshop on Social Network Analysis and MiningDO - 10.5753/brasnam.2021.16131, 2021)
Analyzing electoral trends in political scenarios using social media with data mining techniques has become popular in recent years. A problem in this field is to reliably annotate data during the short period of electoral campaigns. In this paper, we present a methodology to measure labeling divergence and an exploratory analysis of data related to the 2018 Brazilian Presidential Elections. As a result, we point out some of the main characteristics that lead to a high level of divergence during the annotation process in this domain. Our analysis shows a high degree of divergence mainly in regard to sentiment labels. Also, a significant difference was identified between labels obtained by manual annotation and labels obtained using an automatic annotation approach.