Microblog environments like Twitter are increasingly becoming more important to leverage people’s opinion on public events. We aim to predict future public reactions to news events by exploiting related tweets. We define public reactions in terms of their dimension and direction. Our system collects and preprocesses tweets, creates an inverted index to search tweets efficiently, filters them with various methods according to news events; and then uses temporal, spatial and textual features to model predictive classifiers. We also create a public-reaction dataset, BilPredict-2017, which includes several events including terrorist attacks in Turkey from 2015 to 2017. We plan to model ensemble classifiers, and evaluate the success of our system on BilPredict-2017.