Analysis of Political Sentiment Demonstrations on Twitter under COVID-19 Pandemic - Almeida, Yuri Luz De; Alvim, Adriana C. F.; Dias, Vânia Maria Félix (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2023: Anais do XIX Simpósio Brasileiro de Sistemas de Informação, 2023)
Context: In the last decades, there has been a significant expansion of the Internet among the world's population and, together with this growth, social network platforms have consolidated as a trend and established themselves as social and political environments due to their functionalities, low costs and growing number of users. Problem: Given the presented context, it is very important to identify, analyze and bring knowledge about political demonstrations that have taken place on social networks to understand this new era of social and political activism on digital platforms. Solution: In this research, political demonstrations on Twitter were studied with the central objective of bringing knowledge about how they have been organized on this platform. Particularly, in the context of restrictions on the circulation of people resulting from the peak period of the COVID-19 pandemic. IS Theory: This work was elaborated based on concepts of Graph Theory. Method: Posts made on Twitter were monitored and collected during an established period of time, and then the most prominent virtual political demonstrations were selected for analysis. The analysis of the results were carried out with a quantitative approach along with qualitative observations made during the period of data collection. Summary of Results: Based on a defined methodology, it was possible to identify the network structures and actors of the political demonstrations selected for analysis and to show the similarities between these structures. Contributions and Impact in the IS area: The main contributions of this work bring knowledge about the organization of political demonstrations and how related messages are distributed on Twitter, and present an asynchronous data collection methodology.