Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Dimitri Ognibene, Rodrigo Wilkens, Davide Taibi, Davinia Hernández-Leo, Udo Kruschwitz, Gregor Donabauer, Emily Theophilou, Francesco Lomonaco, Sathya Bursic, Rene Alejandro Lobo, J. Roberto Sánchez-Reina, Lidia Scifo, Veronica Schwarze, Johanna Börsting, Ulrich Hoppe, Farbod Aprin, Nils Malzahn, Sabrina Eimler

Research output: Articles / NotesScientific Articlepeer-review

4 Scopus citations

Abstract

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, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.

Original languageEnglish
Article number654930
JournalFrontiers in Artificial Intelligence
Volume5
DOIs
StatePublished - 9 Jan 2023
Externally publishedYes

Keywords

  • collective well-being
  • hierarchical reinforcement learning
  • recommender systems
  • social media
  • social media threats
  • virtual companion

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