Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

• URL: http://arxiv.org/abs/2102.04211v4
• Date: Mon, 17 Oct 2022 13:11:02 GMT
• Title: Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion
• Abstract summary: "Social Media Virtual Companion" combines automatic processing with expert intervention and guidance. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.
• Score: 5.25346752356752
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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. 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.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. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.

Related papers

• SocialRL: Refining LLMs' Social Intelligence through Multi-turn Reinforcement Learning and Reward Design [52.69818441707104]
  Social intelligence enables agents to read social context, infer intent, and adapt over sustained dialogue.<n>Existing reinforcement learning methods optimize single-turn utterances and sparse outcome rewards.<n>We propose SocialRL, a multi-turn reinforcement learning framework addressing both challenges.
  arXiv  Detail & Related papers  (2026-09-09T06:06:34Z)
• ALSO: Adversarial Online Strategy Optimization for Social Agents [63.84788516416339]
  We propose textbfALSO (textbfAdrial ontextbfLine textbfStrategy textbfOptimization) as the first framework for online strategy optimization in social simulation.<n> ALSO formulates multi-turn interaction as an adversarial bandit problem, where combinations of static personas and dynamic strategy instructions are treated as arms.<n>Also introduces a lightweight neural surrogate to predict rewards from interaction histories, enabling sample-efficient exploration and continuous online adaptation.
  arXiv  Detail & Related papers  (2026-05-15T09:25:15Z)
• SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution [82.31558282651811]
  Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents.<n>Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding.<n>We propose SAVOIR, a novel principled framework grounded in cooperative game theory.
  arXiv  Detail & Related papers  (2026-04-21T02:08:25Z)
• Group-Differentiated Discourse on Generative AI in High School Education: A Case Study of Reddit Communities [12.603101932945995]
  We study how different Reddit communities discuss generative AI in high school education.<n>Using 3,789 posts from five education-related subreddits, we compare student, teacher, and mixed communities.<n>Teachers are more likely to articulate explicit pedagogical trade-offs, simultaneously framing AI as both beneficial and harmful for learning.<n>Students more often discuss AI tactically in relation to accusations, grades, and enforcement.
  arXiv  Detail & Related papers  (2026-03-26T03:06:21Z)
• The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook [62.2627874717318]
  Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies.<n>Using a public API snapshot collected about five days after launch, we address three research questions: what AI agents discuss, how they post, and how they interact.<n>We show that agents' writing is predominantly neutral, with positivity appearing in community engagement and assistance-oriented content.
  arXiv  Detail & Related papers  (2026-02-13T05:28:31Z)
• Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool [0.0]
  AI tools can assist in detecting and monitoring harmful content at scale.<n>Few open-source tools offer seamless integration of AI models and social media monitoring infrastructures.<n>This work explores how CSOs can be meaningfully involved in an AI-assisted open-source monitoring tool of anti-democratic movements on Telegram.
  arXiv  Detail & Related papers  (2025-07-09T10:46:58Z)
• Is It Safe To Learn And Share? On Psychological Safety and Social Learning in (Agile) Communities of Practice [4.662447283600293]
  Communities of Practice (CoPs) are increasingly adopted to support social learning beyond formal education.<n> psychological safety, a prerequisite for effective learning, remains insufficiently understood in these settings.<n>This study investigates psychological safety within Agile CoPs through survey data from 143 participants.
  arXiv  Detail & Related papers  (2025-06-30T19:06:43Z)
• A Preliminary Framework for Intersectionality in ML Pipelines [9.937132009954993]
  We argue that machine learning technologies may not provide adequate support for societal identities and experiences.<n>We use this framework to evaluate and report on the (mis)alignments of intersectionality application in machine learning literature.
  arXiv  Detail & Related papers  (2025-05-06T16:57:56Z)
• "Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom [7.915714424668589]
  We argue that understanding the perceptions of those directly or indirectly impacted by Large Language Models in the classroom is essential for ensuring responsible use of AI in this critical domain.<n>We propose the Contextualized Perceptions for the Adoption of LLMs in Education (Co-PALE) framework, which can be used to systematically elicit perceptions.
  arXiv  Detail & Related papers  (2025-02-02T16:50:08Z)
• Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games [47.8980880888222]
  Multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. We propose LASE Learning to balance Altruism and Self-interest based on Empathy. LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship.
  arXiv  Detail & Related papers  (2024-10-10T12:30:56Z)
• Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning [0.18416014644193066]
  AI-driven applications are transforming how students interact with social media. Students engaging with AI-enhanced social media platforms report higher academic performance. AI algorithms effectively match students based on shared academic interests and career goals.
  arXiv  Detail & Related papers  (2024-06-15T01:05:56Z)
• Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems [62.252355444948904]
  This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-04-20T11:07:29Z)
• Socially Responsible Computing in an Introductory Course [2.7426067696238468]
  Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students. We piloted an introductory Java programming course in which activities engaging students in ethical and socially responsible considerations were integrated across modules. The data from the class suggests that the students found the inclusion of the social context in the technical assignments to be more motivating and expressed greater agency in realizing social change.
  arXiv  Detail & Related papers  (2024-01-02T16:52:50Z)
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
  We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
  arXiv  Detail & Related papers  (2023-10-18T02:27:01Z)
• Generative AI trial for nonviolent communication mediation [0.0]
  ChatGPT was used in place of the traditional certified trainer to test the possibility of mediating input sentences. Results indicate that there is potential for the application of generative AI, although not yet at a practical level. It is hoped that the widespread use of NVC mediation using generative AI will lead to the early realization of a mixbiotic society.
  arXiv  Detail & Related papers  (2023-08-07T06:19:29Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)
• Learning Roles with Emergent Social Value Orientations [49.16026283952117]
  This paper introduces the typical "division of labor or roles" mechanism in human society. We provide a promising solution for intertemporal social dilemmas (ISD) with social value orientations (SVO) A novel learning framework, called Learning Roles with Emergent SVOs (RESVO), is proposed to transform the learning of roles into the social value orientation emergence.
  arXiv  Detail & Related papers  (2023-01-31T17:54:09Z)
• Aligning to Social Norms and Values in Interactive Narratives [89.82264844526333]
  We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. We introduce the GALAD agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values.
  arXiv  Detail & Related papers  (2022-05-04T09:54:33Z)
• Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning [0.0]
  We employ a deep reinforcement learning model to optimize the social learning strategies of agents in a cooperative game in a multi-dimensional landscape. We find that the agent spontaneously learns various concepts of social learning, such as copying, focusing on frequent and well-performing neighbors, self-comparison, and the importance of balancing between individual and social learning. We demonstrate the superior performance of the reinforcement learning agent in various environments, including temporally changing environments and real social networks.
  arXiv  Detail & Related papers  (2022-04-26T15:10:27Z)
• Socially-Optimal Mechanism Design for Incentivized Online Learning [32.55657244414989]
  Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment. It is a practically important scenario in many applications such as spectrum sharing, crowdsensing, and edge computing. This paper establishes the incentivized online learning (IOL) framework for this scenario.
  arXiv  Detail & Related papers  (2021-12-29T00:21:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.