Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario


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.

Palavras-chave: labeling divergence, electoral domain, sentiment analysis, manual annotation, offensive speech detection


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SANTOS, Jéssica S.; BERNARDINI, Flávia; PAES, Aline. Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 127-138. ISSN 2595-6094. DOI: