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This work reports about the preliminary results and ongoing research based upon profiling collaborative learning groups of persons within the social micro-blogging platforms like Twitter1 that share potentially common interests on special topic. Hereby the focus is held on spontaneously initiated collaborative learning in Social Media and detection of collaborative learning groups based upon their communication dynamics. Research questions targeted to be answered are: are there any useful data mining algorithms to fulfill the task of pre-selection and clustering of users in social networks, how good do they perform, and what are the metrics that could be used for detection and evaluation in the realm of this task. Basic approach presented here uses as preamble hypothesis that users and their interests in Social Networks can be identified through content generated by them and content they consume. Special focus is held on topic oriented approach as least common bounding point. Those should be also the basic criteria used to detect and outline the learning groups. The aim of this work is to deliver first scientific pre-work for successfully implementation of recommender systems using social network metrics and content features of social network users for the purposes of better learning group communication and information consumption.