In an attempt to anticipate Food Security (FS) crises and overcome the limits of existing early warning systems, predictive models can forecast risk indices by combining heterogeneous data. While using different data sources (e.g., satellite imagery, agroclimatic data, food prices) allows to consider various factors that may impact food crises, the explainability of these models remains challenging. In this work, we propose a Food Security indicator solely based on textual data, discerning among different triggers and accounting for possible biases in the spatial coverage of news. We evaluate our approach on a corpus of French-language documents from Burkina Faso and highlight its significance, paving the way for more open and explainable data sources for monitoring food insecurity.