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

Dimitri Ognibene^{1, 2*}, Davide Taibi³, Udo.Kruschwitz Kruschwitz⁴, Rodrigo Souza Wilkens¹, Davinia Hernandez-Leo⁵, Emily. Theophilou⁵, Lidia Scifo³, Rene Alejandro Lobo⁵, Francesco Lomonaco¹, Sabrina Eimler⁶, H. Ulrich Hoppe⁷ and Nils Malzahn⁸

• ¹University of Milano-Bicocca, Italy
• ²School of Computer Science and Electronic Engineering, Faculty of Science and Health, University of Essex, United Kingdom
• ³Institute of Didactic Technologies, Department of Human and Social Sciences, Cultural Heritage, National Research Council (CNR), Italy
• ⁴University of Regensburg, Germany
• ⁵Pompeu Fabra University, Spain
• ⁶Ruhr University Bochum, Germany
• ⁷Fakultät für Ingenieurwissenschaften, Universität Duisburg-Essen, Germany
• ⁸Ruhr West University of Applied Sciences, Germany

Social media have become an integral part of our lives, expanding our interlinking capabilities
to new levels. There is plenty to be said about their positive effects. On the other hand, however,
some serious negative implications of social media have been repeatedly highlighted in recent
years, pointing at various threats to society and its more vulnerable members, such as teenagers,
in particular, ranging from much-discussed problems such as digital addiction and polarization
to manipulative influences of algorithms and further to more teenager-specific issues (e.g.
body stereotyping). The impact of social media – both at an individual and societal level –
is characterized by the complex interplay between the users’ interactions and the intelligent
components of the platform. Thus users’ understanding of social media mechanisms plays a
determinant role. We thus propose a theoretical framework based on an adaptive “Social Media
Virtual Companion” for educating and supporting an entire community, teenage students, to
interact in social media environments in order to achieve desirable conditions, defined in terms of
a community-specific and participatory designed measure of Collective Well-Being (CWB). This
Companion combines automatic processing with expert intervention and guidance. The virtual
Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB
metric instead of engagement or platform profit, which currently largely drives recommender
systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in
the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized
sequencing of playful learning activities. We put an emphasis on experts and educators in the
educationally managed social media community of the Companion. They play five key roles:
(a) use the Companion in classroom-based educational activities; (b) guide the definition of the
CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will
support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical
reinforcement learning; (d) act as moderators of direct conflicts between the members of the
community; and, finally.