  Due to their widespread adoption, social media platforms present an ideal
environment for studying and understanding social behavior, especially on
information spread. Modeling social media activity has numerous practical
implications such as supporting efforts to analyze strategic information
operations, designing intervention techniques to mitigate disinformation, or
delivering critical information during disaster relief operations. In this
paper we propose a modeling technique that forecasts topic-specific daily
volume of social media activities by using both exogenous signals, such as news
or armed conflicts records, and endogenous data from the social media platform
we model. Empirical evaluations with real datasets from two different platforms
and two different contexts each composed of multiple interrelated topics
demonstrate the effectiveness of our solution.
