Planned Event Forecasting using Future Mentions and Related Entity Extraction in News Articles
This work addresses the problem of predicting disruptive events like protests for administrative officials in democracies such as India, but it is incremental as it builds on existing NLP techniques.
The paper tackles forecasting planned civil unrest events by analyzing news articles, using topic modeling, word2vec, and NER to extract entities and dates, with a proposed method for Related Entity Extraction to identify key participants.
In democracies like India, people are free to express their views and demands. Sometimes this causes situations of civil unrest such as protests, rallies, and marches. These events may be disruptive in nature and are often held without prior permission from the competent authority. Forecasting these events helps administrative officials take necessary action. Usually, protests are announced well in advance to encourage large participation. Therefore, by analyzing such announcements in news articles, planned events can be forecasted beforehand. We developed such a system in this paper to forecast social unrest events using topic modeling and word2vec to filter relevant news articles, and Named Entity Recognition (NER) methods to identify entities such as people, organizations, locations, and dates. Time normalization is applied to convert future date mentions into a standard format. In this paper, we have developed a geographically independent, generalized model to identify key features for filtering civil unrest events. There could be many mentions of entities, but only a few may actually be involved in the event. This paper calls such entities Related Entities and proposes a method to extract them, referred to as Related Entity Extraction.