TY - JOUR
T1 - Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
AU - Ognibene, Dimitri
AU - Wilkens, Rodrigo
AU - Taibi, Davide
AU - Hernández-Leo, Davinia
AU - Kruschwitz, Udo
AU - Donabauer, Gregor
AU - Theophilou, Emily
AU - Lomonaco, Francesco
AU - Bursic, Sathya
AU - Lobo, Rene Alejandro
AU - Sánchez-Reina, J. Roberto
AU - Scifo, Lidia
AU - Schwarze, Veronica
AU - Börsting, Johanna
AU - Hoppe, Ulrich
AU - Aprin, Farbod
AU - Malzahn, Nils
AU - Eimler, Sabrina
N1 - Publisher Copyright:
Copyright © 2023 Ognibene, Wilkens, Taibi, Hernández-Leo, Kruschwitz, Donabauer, Theophilou, Lomonaco, Bursic, Lobo, Sánchez-Reina, Scifo, Schwarze, Börsting, Hoppe, Aprin, Malzahn and Eimler.
PY - 2023/1/9
Y1 - 2023/1/9
N2 - 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.
AB - 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.
KW - collective well-being
KW - hierarchical reinforcement learning
KW - recommender systems
KW - social media
KW - social media threats
KW - virtual companion
UR - http://www.scopus.com/inward/record.url?scp=85147026359&partnerID=8YFLogxK
U2 - 10.3389/frai.2022.654930
DO - 10.3389/frai.2022.654930
M3 - Artículo Científico
AN - SCOPUS:85147026359
SN - 2624-8212
VL - 5
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
M1 - 654930
ER -