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In this paper, we propose a socioscope model for social-network and human-behavior analysis based on mobile-phone call-detail records. Because of the diversity and complexity of human social behavior, no one technique will detect every attribute that arises when humans engage in social behaviors. We use multiple probability and statistical methods for quantifying social groups, relationships, and communication patterns and for detecting human-behavior changes. We propose a new index to measure the level of reciprocity between users and their communication partners. This reciprocity index has application in homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and product marketing. For the validation of our results, we used real-life call logs of 81 users which contain approximately 500 000 h of data on users' location, communication, and device-usage behavior collected over eight months at the Massachusetts Institute of Technology (MIT) by the Reality Mining Project group. Also, call logs of 20 users collected over six months by the University of North Texas (UNT) Network Security team are used. The MIT and UNT data sets contain approximately 5000 callers. The experimental results show that our model is effective.