The research aims to study the temporal variation of generic and specific frames about health in the context of the Covid-19 Pandemic in Brazil. It is a case study conducted on Twitter about health (n = 31,339,922) between March 15 and June 15, 2020. As analytical categories, 3 generic and 3 specific frames about health in epidemic contexts were studied. The generic frames were operationalized deductively: conflict, attribution of responsibility and morality (SEMETKO; VALKENBURG, 2000). The specific frames were operationalized with the inductive method (DE VREESE, 2005): consequences of the pandemic, containment measures, and treatment methods. The tweets were automatically classified using a dictionary-based computational method, ensuring reliability, validity, and reproducibility (KRIPPENDORFF, 2011; SAMPAIO; LYCARIÃO, 2018). A time series was performed to observe the daily variation of the evocation of each frame in the 93 days studied. A temporal network of users connected through mentions, retweets, and replies was also performed, on which the PageRank metric was calculated to measure their daily influence on the network. The ten most prominent actors were selected according to their PageRank on the peak date of each frame of the time series. Finally, information on the context of analysis and on the climate of opinion in Brazil was systematized through representative surveys of the Brazilian population with daily frequency (n = 1,800, ME = plus–minus 2 percent, CI = 95 percent). The results indicate that the order of evocation of the frames, from most to least prominent, was conflict, attribution of responsibility, consequences of the pandemic, morality, containment measures, and treatment methods. In particular, the frames of conflict, attribution of responsibility, and consequences of the pandemic were strongly related to a negative, episodic, and human interest framing of the tweets. On the other hand, the other frames (morality, containment measures and treatment methods), prioritized thematic framings, whose implications were preponderantly of a more social and less individual nature. Also noteworthy is the significant presence of anonymous profiles among the Top10 users of each frame, as well as activists, health experts, digital influencers, journalists, media organizations, politicians, and profiles of another nature, such as fandoms and satirical. Finally, regarding the study of the conditions that explain the highest peaks of evocation of the generic and specific frames in the time series, a phenomenon that was defined in this thesis as framing synchronization was observed. Framing synchronization is defined as the phenomenon of collective adjustment of the frequency of network activation of a given frame through interaction between individuals and influenced by five factors: context, succession of associated events, climate of opinion, and the combination of frames, and issues.