In recent years, the availability of a vast amount of user-generated data via social media, has given an opportunity to researchers for analyzing these data sources and discovering meaningful information. However, processing and understanding this immense amount of data is challenging and calls for automated approaches, and involvement of field experts to use their field knowledge and experience to enhance the data analysis. So far, existing approaches only enable the detection of indicative information from the data such as the occurrence of critical incidents, relevant situation reports etc. Consequently, the next step would be to better relate the user provided information to the real-world quantities. In this work, a predictive visual analytics approach is developed that offers semi-automated methods to estimate quantitative information (e.g. number of people who participate in a public event). At first, the approach provides interactive visual tools to explore social media data in time and space and select features required as input for training and prediction interactively. Next, a suitable model can be trained based on these feature sets and applied for prediction. Finally, the approach also allows to visually explore prediction results and measure quality of predictions with respect to the ground truth information obtained from past observations. The result of this work is a generic visual analytics approach, that provides expert user with visual tools for a constant interaction between human and machine, for producing quantitative predictions based on social media data. The results of predictions are promising, especially in cases that the location, time and other related information to public events are considered together with the content of user-generated data.