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Recognizing that a community's capability to respond to and recover from disaster depends partly on the strength and effectiveness of its social networks, social network analysis (SNA) has risen to a field having important implications. In particular, many disaster and emergency recovery operations now consider the information provided by social networks to be one of their prime sources of data. The task of integrated social information engineering is to fuse that data to yield meaningful knowledge devoid of contradiction and provide a pathway for discovery of related information. Computing with Words represents an attempt to fuse linguistic information using possibilistic analysis. The approach entails machine learning that fuses contexts consisting of essentially symbolic information for the prediction of an appropriate action(s). SNA is facilitated because the method allows large contexts and crowd sourced approaches, consisting of distinct textual phrases, to be mapped to similar prior experiential knowledge. If this knowledge proves to be erroneous for any reason, then it is a simple matter to supply the correct knowledge for non monotonic learning to occur. The method also supplies an associated possibility, allows for proper responses to be forthcoming, and can be trained / run in parallel. In summary, the paper proper considers the burgeoning field of social information engineering and the automation of its integration by way of transformative reuse.