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The paper presents a conceptual model for social sensor design in socially-competent computing systems. The model is based on theories of social behavior being driven by the underlying attitudes, rather than on models predicting behavior in response to behavior representing people as physical objects in dynamic interactions. It is proposed to increase the ability of the systems to extract relevant features and to achieve better social competence, similar to the kind that is underlying human interactions by implementing algorithms, capable of predicting behavior in response to attitude. The paper presents an account of the social level of understanding human interactions in the context of three application scenarios-multi-hop communication networks, embedded systems for support of medical interventions and information systems supporting educational activities. Patterns of real data are discussed in terms of the proposed model of social sensor design for enhanced socially-competent computing.