  Forecasting social media activity can be of practical use in many scenarios,
from understanding trends, such as which topics are likely to engage more users
in the coming week, to identifying unusual behavior, such as coordinated
information operations or PumpNDump efforts. To evaluate a new approach to
forecasting, it is important to have baselines against which to assess
performance gains. We experimentally evaluate the performance of four baselines
for forecasting activity in several social media datasets that record
discussions related to three different geo-political contexts synchronously
taking place on two different platforms, Twitter and YouTube. Experiments are
done over hourly time periods. Our evaluation identifies the baselines which
are most accurate for particular metrics and thus provide guidance for future
work in social media modeling.
