Social media activity forecasting with exogenous and endogenous signals

Modeling social media activity has numerous practical implications such as in helping 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 multiplexing different exogenous signals, such as news reports and armed conflicts records, and endogenous data from the social media platform we model. For this, we trained a collection of LSTM models, each leveraging a different exogenous source, and dynamically select one model for each topic. Empirical evaluations with real datasets from two social media platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.