The existence of polarization and echo chambers has been noted in social media discussions of public concern such as the Covid-19 pandemic, foreign election interference, and regional conflicts. However, measuring polarization and assessing the manner in which polarization contributes to partisan behavior is not always possible to evaluate with static network or affect measurements. To address this, we conduct an analysis of two large Twitter datasets collected around Covid-19 vaccination and the Ukraine war to investigate polarization in terms of the evolution in influencer preferences and toxicity of post contents. By reducing retweet behavior in each sample to several key dimensions, we identify clusters that reflect ideological preferences, along with geographic or linguistic separation for some cases. By tracking the central retweet tendency of these clusters over time, we observe differences in the relative position of ideologically unaligned clusters compared to aligned ones, which we interpret as reflecting polarization dynamics in the information diffusion space. We then measure the toxicity of posts and test if toxicity in one cluster can be temporally dependent on its structural closeness to (or toxicity of) another. We find evidence of ideological opposition among clusters of users in both samples, and a temporal association between toxicity and structural divergence for at least two ideologically opposed clusters in our samples. These observations support the importance of analyzing polarization as a multifaceted dynamic phenomenon where polarization dynamics may also manifest in unexpected ways such as within a single ideological camp.