text: in the type of online discourse that potentially forms the data that 
  underpins LMs. 
  
  An important caveat is that social movements which are poorly 
  documented and which do not receive significant media attention 
  will not be captured at all. Media coverage can fail to cover protest 
  events and social movements [41, 96] and can distort events that 
  challenge state power [36]. This is exemplified by media outlets 
  that tend to ignore peaceful protest activity and instead focus on 
  dramatic or violent events that make for good television but nearly 
  always result in critical coverage [81]. As a result, the data under- 
  pinning LMs stands to misrepresent social movements and dispro- 
  portionately align with existing regimes of power. 
  
  Developing and shifting frames stand to be learned in incomplete 
  ways or lost in the big-ness of data used to train large LMs — particu- 
  larly if the training data isn’t continually updated. Given the com- 
  pute costs alone of training large LMs, it likely isn’t feasible for 
  even large corporations to fully retrain them frequently enough to 
  keep up with the kind of language change discussed here. Perhaps 
  fine-tuning approaches could be used to retrain LMs, but here again, 
  what would be required is thoughtful curation practices to find ap- 
  propriate data to capture reframings and techniques for evaluating 
  whether such fine-tuning appropriately captures the ways in which 
  new framings contest hegemonic representations. 