CARAMEL: Ecosystem for Big Social Data
Context: A large volume of data produced in social media is analyzed through different perspectives. Much effort goes into retrieving and processing the data, maintaining the necessary infrastructure, and building and sharing the foundation between actors with different roles. These challenges are observed in data ecosystems. Problem: The central systems to support data analysis from social networks have some restrictions (data collection, sharing, reuse, etc.). Data collection and analysis require technical skills that some users need and do not have, impacting the quality of inferences, accounting, and conclusions. Solution: We propose an architecture for “Big Social Data” ecosystems considering the collaborative construction of data extraction and sharing mechanisms. IS Theory: This proposal is related to “knowledge-based theory,” as much knowledge can be inferred from social data. It also supports the Externalization and Combination steps of the Organizational knowledge creation model. Method: We observe aspects related to data analysis, considering the reuse of the mechanisms created and the sharing of bases that can run and be stored in a distributed way to meet even instantaneous analysis. Results: The architecture was implemented to work in a distributed way, contains a collector and a filter and allows data sharing. A data collection test was conducted during the 2022 presidential elections in Brazil. Contributions: The main contribution is the architecture of a Big Social Data Ecosystem, focused on the evolution of social data analysis that also observes the interoperability between distributed solutions. The technological contributions are an instance of this architecture for the cloud, social media data collectors, and datasets of the 2022 election in Brazil.
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