In recent years, the notion of the Metaverse has become the focus of a growing body of work in the industry. However, there is no consensus on the conceptualization in academia. To date, much of this attention has revolved around technological challenges.However, what is notably missing from these discussions is a consideration of the human factors and social aspects that are considered more critical challenges within HCI. The aims of this SIG are as follows: Firstly, to provide a platform for researchers and practitioners to engage with the various definitions and the ways in which the Metaverse is developing. Secondly, to discuss the opportunities, challenges, and future possibilities in the context of HCI. This will lay the foundations to build a network for academics interested in the field for future multidisciplinary research relating to the Metaverse. CCS Concepts: • Human-centered computing → Human computer interaction (HCI).
Intangible Cultural Heritage is at a continuous risk of extinction. Where historical artefacts engine the machinery of intercontinental mass-tourism, socio-technical changes are reshaping the anthropomorphic landscapes everywhere on the globe, at an unprecedented rate. There is an increasing urge to tap into the hidden semantics and the anecdotes surrounding people, memories and places. The vast cultural knowledge made of testimony, oral history and traditions constitutes a rich cultural ontology tying together human beings, times, and situations. Altogether, these complex, multidimensional features make the task of data-mapping of intangible cultural heritage a problem of sustainability and preservation. This paper addresses a suggested route for conceiving, designing and appraising a digital framework intended to support the conservation of the intangible experience, from a user and a collective-centred perspective. The framework is designed to help capture the intangible cultural value of all places exhibiting cultural-historical significance, supported by an extensive analysis of the literature. We present a set of design recommendations for designing mobile apps that are intended to converge crowdsourcing to Intangible Cultural Heritage.
As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates.
As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.
Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user-and group-centric focus.
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