  Forecasting events like civil unrest movements, disease outbreaks, financial
market movements and government elections from open source indicators such as
news feeds and social media streams is an important and challenging problem.
From the perspective of human analysts and policy makers, forecasting
algorithms need to provide supporting evidence and identify the causes related
to the event of interest. We develop a novel multiple instance learning based
approach that jointly tackles the problem of identifying evidence-based
precursors and forecasts events into the future. Specifically, given a
collection of streaming news articles from multiple sources we develop a nested
multiple instance learning approach to forecast significant societal events
across three countries in Latin America. Our algorithm is able to identify news
articles considered as precursors for a protest. Our empirical evaluation shows
the strengths of our proposed approaches in filtering candidate precursors,
forecasting the occurrence of events with a lead time and predicting the
characteristics of different events in comparison to several other
formulations. We demonstrate through case studies the effectiveness of our
proposed model in filtering the candidate precursors for inspection by a human
analyst.
