WebCiência IQ-UFG: multi-scale temporal patterns during the transpandemic
DOI:
https://doi.org/10.21284/elo.v14i.20014Keywords:
Science outreach, Social media, YouTube®, ChemistryAbstract
Social networks generate structured data in space and time, making it possible to analyse complex processes within a community. The enormous flow of information, however, requires methods for data acquisition and analysis over time. In this context, the temporal pattern in the performance of the YouTube scientific dissemination channel WebCiência IQ-UFG was evaluated during the transpandemic. To model the influence of time on multiple scales, Moran’s Eigenvector Maps (MEM) were used in redundancy analyses and multivariate variograms. The results indicated that channel’s metrics performance was best represented by MEM generated from connections based on relative temporal distance and weighted non-linearly with webinars’ posting dates. Temporal structures were mainly related to type of teaching during the transpandemic and to the management of the extension action (large scale), as well as to academic cycles and webinar years (intermediate scale). In addition to modelling multiple scales, MEM controlled the increasing variance in performance metrics between neighbouring webinars over time, in addition to the unidirectional trend of the temporal coordinate.
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