In traditional data fusion hard physical sensor data has been the main source of information. This has changed during the past decade, under the backdrop of counter insurgency (COIN). In the COIN environment the majority of information comes from human sources (soft data). The source of this information can be human informants or soldiers conducting reconnaissance in the field. This human sourced soft data is filled with vast amounts of valuable information. Recently a large number of Natural Language Processing techniques have been developed to process this soft data into the form of relational graphs. In this paper we have described various graph analytical techniques that can be applied towards fusion of hard and soft information and understanding the situations of interest by an analyst. The processing elements exhibited in this paper are association of entities and relations in observational hard and soft data graphs to form the cumulative data graph, situation assessment via graph matching of situations of interest against the cumulative data graph, and social network analysis to identify and extract high value individuals in the network. To illustrate these graph analytic tools we have used the Sunni message thread of SYNCOIN consisting of 114 soft messages and 4 hard data reports. The value of this work has been demonstrated with detailed analysis and examples from the aforementioned dataset.