  Quantifying regularities in behavioral dynamics is of crucial interest for
understanding collective social events such as panics or political revolutions.
With the widespread use of digital communication media it has become possible
to study massive data streams of user-created content in which individuals
express their sentiments, often towards a specific topic. Here we investigate
messages from various online media created in response to major, collectively
followed events such as sport tournaments, presidential elections or a large
snow storm. We relate content length and message rate, and find a systematic
correlation during events which can be described by a power law relation - the
higher the excitation the shorter the messages. We show that on the one hand
this effect can be observed in the behavior of most regular users, and on the
other hand is accentuated by the engagement of additional user demographics who
only post during phases of high collective activity. Further, we identify the
distributions of content lengths as lognormals in line with statistical
linguistics, and suggest a phenomenological law for the systematic dependence
of the message rate to the lognormal mean parameter. Our measurements have
practical implications for the design of micro-blogging and messaging services.
In the case of the existing service Twitter, we show that the imposed limit of
140 characters per message currently leads to a substantial fraction of
possibly dissatisfying to compose tweets that need to be truncated by their
users.
