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 inter-linking 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 for 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 full impact of current social media platform design -both at an individual and societal level -asks for a more holistic approach to tackle the problems conceptually. The way forward we see is to extend measures of Collective Well-Being (CWB) to social media communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting an entire community, teenage students, to interact with social media. 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 1

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Making Sense of Subtitles: Sentence Boundary Detection and Speaker Change Detection in Unpunctuated Texts

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