Forecasting topic activity with exogenous and endogenous information signals in Twitter

Abstract

Modeling social media activity has numerous practical implications such as designing and testing 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 Twitter on two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.