News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a wide variety of online news portals and performs various analysis tasks. The framework initially identifies fresh news (first stories) and clusters articles about the same incidents. For every story, at first, it extracts all of the corresponding triples and, then, it creates a knowledge base (KB) using open information extraction techniques. This knowledge base is then used to create a summary for the user. News Monitor allows for the users to use it as a search engine, ask their questions in their natural language and receive answers that have been created by the state-of-the-art framework BERT. In addition, News Monitor crawls the Twitter stream using a dynamic set of “trending” keywords in order to retrieve all messages relevant to the news. The framework is distributed, online and performs analysis in real-time. According to the evaluation results, the fake news detection techniques utilized by News Monitor allow for a F-measure of 82% in the rumor identification task and an accuracy of 92% in the stance detection tasks. The major contribution of this work can be summarized as a novel real-time and scalable architecture that combines various effective techniques under a news analysis framework.

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.