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

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.

The Role of Educational Interventions in Facing Social Media Threats: Overarching Principles of the COURAGE Project

“…The categorization of AI applications proposed by Zawacki-Richter in [42] identified four categories: application for profiling and prediction, application to support assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. The COURAGE conceptual framework provides different contributions in these four areas with specific modules, in which the interconnection between the theoretical pedagogical strategies and the educational application of AI approaches plays a relevant role [43].…”

“…As AI algorithms of social media platforms do not directly counter threats for teenagers but may actually worsen and amplify them, in the COURAGE project we propose the adoption of a virtual learning companion (VLC) powered by an AI system that counters these threats and the platform specific algorithmic hazards [43]. The VLC AI system developed comprises adaptive detectors of content and network threats, user models to support personalized interventions as well as content and educational activity recommendations.…”

“…The VLC also supports educators in empowering teenagers regarding the threats in social media by developing social media competences and self-protection skills. A particular emphasis was put on the role that experts and educators play in the management of the VLC by driving and moderating the use of it in classroom-based educational activities as well as providing learning strategies, objectives and activities implemented in the VLC [43][44][45]. In the next section we present how the VLC has been piloted in real scenarios through different educational interventions.…”

“…The efficiency of more advanced, multi-step user specific, algorithmic interventions, e.g., content and connection recommendations, to a) counter network and algorithmic threats, b) support the achievement of educational targets, and c) improve the collective well-being of the user community is currently supported by simulations based on collected data and validated user models [43].…”

Moving Beyond Benchmarks and Competitions: Towards Addressing Social Media Challenges in an Educational Context

“…Our starting point is the observation that social media users often have a limited understanding of the platforms and their algorithms and, more importantly, the effects of their actions on others' experiences and the proliferation of toxic phenomena [66,123]. We present a framework that serves as a machine-learningbased social media education tool that aims at integrating solutions to the above-mentioned problems directly in the users' social media experience [96] 1 . As such the user's feed is augmented automatically with additional information on the content and underlying producing social network, as can be seen in Fig.…”