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Social media have become an integral part of our lives, expanding ourinterlinking capabilities to new levels. There is plenty to be said about theirpositive effects. On the other hand, however, some serious negativeimplications of social media have been repeatedly highlighted in recent years,pointing at various threats to society and its more vulnerable members, such asteenagers. We thus propose a theoretical framework based on an adaptive "SocialMedia Virtual Companion" for educating and supporting an entire community,teenage students, to interact in social media environments in order to achievedesirable conditions, defined in terms of a community-specific andparticipatory designed measure of Collective Well-Being (CWB). This Companioncombines automatic processing with expert intervention and guidance. Thevirtual Companion will be powered by a Recommender System (CWB-RS) that willoptimize a CWB metric instead of engagement or platform profit, which currentlylargely drives recommender systems thereby disregarding any societal collateraleffect.We put an emphasis on experts and educators in the educationally managedsocial media community of the Companion. They play five key roles: (a) use theCompanion in classroom-based educational activities; (b) guide the definitionof the CWB; (c) provide a hierarchical structure of learning strategies,objectives and activities that will support and contain the adaptive sequencingalgorithms of the CWB-RS based on hierarchical reinforcement learning; (d) actas moderators of direct conflicts between the members of the community; and,finally, (e) monitor and address ethical and educational issues that are beyondthe intelligent agent's competence and control. Preliminary results on theperformance of the Companion's components and studies of the educational andpsychological underlying principles are presented.

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion