Mobile phones are playing an ever growing role in the today's technology-assisted society as they support, maintain, ease and even influence our real life's social behavior and interactions. The analysis and understanding of human behavior and sociality on mobile phones have become an emerging field of research and a few works have recently offered empirical evidence of their nature by analyzing the phone call and/or text message communication patterns of large datasets. Although these studies have drawn attention to a few aspects that influence and characterize human interactions by mobile phones, none of these results are mirrored into any on-board feature of modern smartphones. This paper describes a new application, named Groo-Me to recall the notion of Dunbar's grooming coalition, whose principal aim is to fill the gap. We motivate the app functionalities on the base of an extensive analysis of a large anonymized dataset of Call Detail Records (CDR) containing call and text message activities of almost 1m mobile subscribers of a large mobile operator. The application is available for Android-based smartphones and maintains the user's personal network, helps users to control their core sociality, exploits burstiness and periodicity to predict the next user's activity by dynamically updating the list of favorite within a given time window, collects statistics about the user's interactions on different channels.
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