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A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter. Mining such reflections is interesting to both policy makers and ordinary people seeking information. In this paper, we propose a unified framework to mine multi-aspect reflections of news events in Twitter. We propose a novel and efficient dynamic hierarchical entity-aware event discovery model to learn news events and their multiple aspects. The aspects of an event are linked to their reflections in Twitter by a bootstrapped dataless classification scheme, which elegantly handles the challenges of selecting informative tweets under overwhelming noise and bridging the vocabularies of news and tweets. In addition, we demonstrate that our framework naturally generates an informative presentation of each event with entity graphs, time spans, news summaries and tweet highlights to facilitate user digestion.

Today's military operations require information from an unprecedented number of sources resulting in an overwhelming volume of collected data. A primary challenge for military commanders and their staff is separating the important information from the routine. Currently, the Value of Information (VOI) assigned a piece of information is a multiple step process requiring intelligence collectors and analysts to judge its value within a host of differing operational situations. The cognitive processes behind these conclusions resist codification with exact precision suggesting that new methodologies are required to deal with this significant issue. This paper presents an approach for calculating the VOI in complex military environments using a fuzzy associative memory model as an effective framework for contextually tuning the VOI based on the information's content, source reliability and latency