Ficções no twitter: as transformações nos memeplexos das interações informacionais referentes ao processo de impeachment da presidenta Dilma Rousseff

Abstract

Considering that, between the 20th and 21st centuries, technological advances in mass media contributed to the growth of the flow of information mediated by algorithms, it is necessary to recognize that this type of communication enhanced the context of debates and political engagement thanks to the popularization of the use of smartphones and online platforms. This opens up the need to investigate the relationships and processes that underlie this informational context in its symbolic, affective and cognitive dimensions to understand the aspects that underlie this new informational scenario. The approach was based on a proposal to analyze a political event with major repercussions in the media, the impeachment process of President Dilma Rousseff. In particular, the aim was to analyse fictions understood as useful tools for describing how individuals use the imaginary to create or adopt narratives that simplify and organize their experiences, created in the context of posts on two political Twitter groups - Mídia Ninja and Movimento Brasil Livre - related to the impeachment process. An approach was proposed based on identifying and analyzing the smallest units of replicable information present in this flow of communication (considering them here as <memes= from the perspective of British biologist Richard Dawkins). The intention is to assess how this replicable information has evolved since the President's re-election until her impeachment trial. In order to carry out this analysis, it was essential to gather a set of data that would allow us not only to examine the content, but also the context of the information. This <social data= was obtained using an Application Programming Interface (API) made available by Twitter in 2020. Data was accessed and collected by developing scripts implemented in the Python programming language, following the guidelines of the Tweepy Documentation library. Data analysis followed the Data Mining methodology and in this specific study Text Mining with the support of the Orange Canvas program. As a result, the study highlighted the competition between memes for attention and influence online and how, through them, it is possible to perceive the symbolic environment that makes up the fictions created through the narratives contained in the messages. The study encountered some technical difficulties, tested possible tools for identifying memetic variations, and proposed future research to improve understanding of informational and cultural processes in the digital age, including longitudinal studies in social network analysis and the application of cladistic models to understand the evolution of memes. Finally, we conclude that the study offers important contributions to transdisciplinary empirical studies that integrate fields such as Biology, Information Science, Computer Science, Communication, Psychology, and other areas of the Humanities and Applied Social Sciences, and proposes methodological alternatives for empirical studies that aim to incorporate a new perspective into the epistemological field of Information Science.