Abstract. The volume and velocity of available online sources have changed journalistic research in terms of cost and effort required for discovering stories. However, the heterogeneity and veracity of data sources pose further obstacles in knowledge extraction making it a hard task to handle. The purpose of this study is threefold. Firstly, we present a platform for automated data processing in the context of Computational Journalism. We then propose a general methodology for event extraction from different data sources. Finally, we conducted a pilot implementation of our methodology for protest events extraction from news and Twitter data. Evaluation showed promising results, indicating the feasibility of our approach.