Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag \#BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans. In response to \#BlackLivesMatter, other Twitter users have adopted \#AllLivesMatter, a counter-protest hashtag whose content argues that equal attention should be given to all lives regardless of race. Through a multi-level analysis, we study how these protests and counter-protests diverge by quantifying aspects of their discourse. In particular, we introduce methodology that not only quantifies these divergences, but also reveals whether they are from widespread discussion or a few popular retweets within these groups. We find that \#BlackLivesMatter exhibits many informationally rich conversations, while those within \#AllLivesMatter are more muted and susceptible to hijacking. We also show that the discussion within \#BlackLivesMatter is more likely to center around the deaths of Black Americans, while that of \#AllLivesMatter is more likely to sympathize with the lives of police officers and express politically conservative views.