  Social media datasets make it possible to rapidly quantify collective
attention to emerging topics and breaking news, such as crisis events.
Collective attention is typically measured by aggregate counts, such as the
number of posts that mention a name or hashtag. But according to rationalist
models of natural language communication, the collective salience of each
entity will be expressed not only in how often it is mentioned, but in the form
that those mentions take. This is because natural language communication is
premised on (and customized to) the expectations that speakers and writers have
about how their messages will be interpreted by the intended audience. We test
this idea by conducting a large-scale analysis of public online discussions of
breaking news events on Facebook and Twitter, focusing on five recent crisis
events. We examine how people refer to locations, focusing specifically on
contextual descriptors, such as "San Juan" versus "San Juan, Puerto Rico."
Rationalist accounts of natural language communication predict that such
descriptors will be unnecessary (and therefore omitted) when the named entity
is expected to have high prior salience to the reader. We find that the use of
contextual descriptors is indeed associated with proxies for social and
informational expectations, including macro-level factors like the location's
global salience and micro-level factors like audience engagement. We also find
a consistent decrease in descriptor context use over the lifespan of each
crisis event. These findings provide evidence about how social media users
communicate with their audiences, and point towards more fine-grained models of
collective attention that may help researchers and crisis response
organizations to better understand public perception of unfolding crisis
events.
