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

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, poi...

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Autores principales: Ognibene, Dimitri, Wilkens, Rodrigo, Taibi, Davide, Hernández-Leo, Davinia, Kruschwitz, Udo, Donabauer, Gregor, Theophilou, Emily, Lomonaco, Francesco, Bursic, Sathya, Lobo, Rene Alejandro, Sánchez-Reina, J. Roberto, Scifo, Lidia, Schwarze, Veronica, Börsting, Johanna, Hoppe, Ulrich, Aprin, Farbod, Malzahn, Nils, Eimler, Sabrina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869176/
https://www.ncbi.nlm.nih.gov/pubmed/36699613
http://dx.doi.org/10.3389/frai.2022.654930
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author Ognibene, Dimitri
Wilkens, Rodrigo
Taibi, Davide
Hernández-Leo, Davinia
Kruschwitz, Udo
Donabauer, Gregor
Theophilou, Emily
Lomonaco, Francesco
Bursic, Sathya
Lobo, Rene Alejandro
Sánchez-Reina, J. Roberto
Scifo, Lidia
Schwarze, Veronica
Börsting, Johanna
Hoppe, Ulrich
Aprin, Farbod
Malzahn, Nils
Eimler, Sabrina
author_facet Ognibene, Dimitri
Wilkens, Rodrigo
Taibi, Davide
Hernández-Leo, Davinia
Kruschwitz, Udo
Donabauer, Gregor
Theophilou, Emily
Lomonaco, Francesco
Bursic, Sathya
Lobo, Rene Alejandro
Sánchez-Reina, J. Roberto
Scifo, Lidia
Schwarze, Veronica
Börsting, Johanna
Hoppe, Ulrich
Aprin, Farbod
Malzahn, Nils
Eimler, Sabrina
author_sort Ognibene, Dimitri
collection PubMed
description 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.
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spelling pubmed-98691762023-01-24 Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion Ognibene, Dimitri Wilkens, Rodrigo Taibi, Davide Hernández-Leo, Davinia Kruschwitz, Udo Donabauer, Gregor Theophilou, Emily Lomonaco, Francesco Bursic, Sathya Lobo, Rene Alejandro Sánchez-Reina, J. Roberto Scifo, Lidia Schwarze, Veronica Börsting, Johanna Hoppe, Ulrich Aprin, Farbod Malzahn, Nils Eimler, Sabrina Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9869176/ /pubmed/36699613 http://dx.doi.org/10.3389/frai.2022.654930 Text en Copyright © 2023 Ognibene, Wilkens, Taibi, Hernández-Leo, Kruschwitz, Donabauer, Theophilou, Lomonaco, Bursic, Lobo, Sánchez-Reina, Scifo, Schwarze, Börsting, Hoppe, Aprin, Malzahn and Eimler. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Artificial Intelligence
Ognibene, Dimitri
Wilkens, Rodrigo
Taibi, Davide
Hernández-Leo, Davinia
Kruschwitz, Udo
Donabauer, Gregor
Theophilou, Emily
Lomonaco, Francesco
Bursic, Sathya
Lobo, Rene Alejandro
Sánchez-Reina, J. Roberto
Scifo, Lidia
Schwarze, Veronica
Börsting, Johanna
Hoppe, Ulrich
Aprin, Farbod
Malzahn, Nils
Eimler, Sabrina
Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title_full Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title_fullStr Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title_full_unstemmed Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title_short Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
title_sort challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869176/
https://www.ncbi.nlm.nih.gov/pubmed/36699613
http://dx.doi.org/10.3389/frai.2022.654930
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