Sticks and Stones May Break My Bones, but Will Comments Ever Hurt Me? : a Burkean Analysis of Cancel Culture in Social Media Spaces

In the modern social media era, “cancel culture” is a growing phenomenon used to hold public figures accountable for perceived wrongdoings. But who gets to cancel these celebrities? And how does someone get canceled? The concept of “cancel culture” has often been discussed in the past in the sense that the United States is a culture that is prone to want to cancel others. In my study, I am looking at the fact that there is a desire to cancel a perceived wrongdoer from an online space that is functioning as its own individual culture. The terms used to describe cancel culture by scholars and online users are often imprecise, so this thesis seeks to provide a clear distinction on what cancel culture and being canceled means and provide a new concept to help explain the phenomenon: a call for cancelation. YouTubers Logan Paul and Tana Mongeau and their apology videos’ comments sections serve as the focus of this thesis that rhetorically analyzes the culture calling for cancelation in an online space. The findings of this thesis help contribute to scholarly and industry knowledge of cancel culture: what it is, how to define it, related concepts, who is calling for cancelation, and what may happen when a public figure receives a call for cancelation from the supporting culture

A robust iterative algorithm for image restoration

Abstract We present a new image restoration method by combining iterative VanCittert algorithm with noise reduction modeling. Our approach enables decoupling between deblurring and denoising during the restoration process, so allows any well-established noise reduction operator to be implemented in our model, independent of the VanCittert deblurring operation. Such an approach has led to an analytic expression for error estimation of the restored images in our method as well as simple parameter setting for real applications, both of which are hard to attain in many regularization-based methods. Numerical experiments show that our method can achieve good balance between structure recovery and noise reduction, and perform close to the level of the state of the art method and favorably compared to many other methods