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

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• dc.contributor.author Ognibene, Dimitri
• dc.contributor.author Wilkens, Rodrigo
• dc.contributor.author Taibi, Davide
• dc.contributor.author Hernández Leo, Davinia
• dc.contributor.author Kruschwitz, Udo
• dc.contributor.author Donabauer, Gregor
• dc.contributor.author Theophilou, Emily
• dc.contributor.author Lomonaco, Francesco
• dc.contributor.author Bursic, Sathya
• dc.contributor.author Lobo Quintero, René Alejandro
• dc.contributor.author Sánchez Reina, Jesús Roberto
• dc.contributor.author Scifo, Lidia
• dc.contributor.author Schwarze, Veronica
• dc.contributor.author Börsting, Johanna
• dc.contributor.author Hoppe, H. Ulrich
• dc.contributor.author Aprin, Farbod
• dc.contributor.author Malzahn, Nils
• dc.contributor.author Eimler, Sabrina
• dc.date.accessioned 2023-03-21T07:05:51Z
• dc.date.available 2023-03-21T07:05:51Z
• dc.date.issued 2023
• dc.description.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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ognibene D, Wilkens R, Taibi D, Hernández-Leo D, Kruschwitz U, Donabauer G, et al. Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion. Front Artif Intell. 2023 Jan 9;5:654930. DOI: 10.3389/frai.2022.654930
• dc.identifier.doi http://dx.doi.org/10.3389/frai.2022.654930
• dc.identifier.issn 2624-8212
• dc.identifier.uri http://hdl.handle.net/10230/56293
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Artificial Intelligence. 2023 Jan 9;5:654930
• dc.rights © 2023 Ognibene, Wilkens, Taibi, Hernández-Leo, Kruschwitz, Donabauer, Theophilou, Lomonaco, Bursic, Lobo, Sánchez-Reina, Scifo, Schwarze, Börsting, Hoppe, Aprin, Malzahn and Eimler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Collective well-being
• dc.subject.keyword Recommender systems
• dc.subject.keyword Social media
• dc.subject.keyword Virtual companion
• dc.subject.keyword Social media threats
• dc.subject.keyword Hierarchical reinforcement learning
• dc.title Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion