The polarization of ideas is a natural phenomenon of human behavior. When productive, it can generate scenarios for reflection on arguments, opinions, and interactions, but when polarization is extreme, it generates social isolation, manipulation, perception of injustice, unproductive debates, certainties of absolute truths, fanaticism, feelings of hatred, and even wars. Deconstructing the ecosystem of extreme polarization directly benefits social stability. Virtual social environments are excellent scenarios for fostering debates, negotiations, and the exposition of ideas. The objective of this study is to use the same virtual social ecosystem with its empowerment mechanisms and influence attributes to support the creation of tools, functionalities, and simulations to develop healthier systems. The knowledge generated supported the development of a design and cognitive ethical affordances practiced in metacognitive design, optimized navigation, and metacognitive simulations. The cognitive, ethical affordances incorporated into the Productive Reinforcement Spiral method model were part of a metacognitive simulation of collaborative negotiation with a productive reinforcement spiral mechanism, whose description and evaluation of the design are the main objectives of this paper.

This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.

This paper proposes an agenda of possible solutions relevant to the challenge of polarization of extreme positions. The challenge of mitigating polarization of extreme positions has been a hot topic in politics, journalism, new media, and, more recently, in CSCW and HCI research. In this study, the main focus is to investigate the harmful effects of polarization from a neuroscience point of view. The suggested solutions are based on theories of social cognitive neuroscience and encourage awareness of inappropriate behavior, an increase in the diversity of arguments, and the exercise of individual and collective responsibility, to mitigate the problem of polarization that threatens public debate and, consequently, democracy.

In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics.

In this paper, we report on the design and development of a collaborative application for mobile devices, built by elementary and undergraduate students, whose goal was to help comply with Covid-19 protocols on the occasion of the resumption of face-to-face classes in schools in the city of Macaé, in Rio de Janeiro. Collective intelligence guided both the problem-solving activity (application construction) and collective inspection by students (application use) of the actions needed to prevent the spread of the Covid-19 virus.

Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

Recently, crowdsourcing platforms have been used to solve problems in the field of urban planning by involving crowds of citizens in performing tasks. However, the success of this approach is directly related to how work is managed. The goal of the present study is to make a broad characterization of work management in crowdsourcing approaches applied to urban planning through a systematic literature review. More specifically, we aim to investigate aspects related to the quality of work.

Knowledge management process includes capturing, representing and disseminating knowledge among people within an organization. One typical technique used to collect knowledge is Lessons Learned. However, this technique has shown ineffective. People do not really read traditional Lesson Learned reports. On the other side, Storytelling is a technique that has been getting attention in organizations for a long time. Due to its playfulness, stories raise can attract the reader and contribute to triggering tacit knowledge, facilitating communication, stimulating exchange and creativity. Thus, it could be a useful tool to capture lessons learned. This paper proposes a collaborative creation method for collecting lessons learned that focuses on the well-known characteristics and structure of good stories, supported by a collaborative system. The main contribution of this research is an innovative method for group storytelling based on the hero's journey approach.

In recent years, social curation technology has been successfully promoted as a means of addressing the problem of involving citizens with news content. Sites like Facebook., Reddit and Storify., just to name a few, allow their users to discuss, comment., share, identify and review news content from various sources. A key goal of this paper is to analyze the process of social validation in the context of Acropolis, a social computing platform that allows citizens to build and share their own narratives about complex or long-term news stories. The sense of social validation determines an increase in self-esteem, self-confidence, and instant happiness, when conveniently engendered in virtual environments.

Acropolis is a social computing platform especially designed to engage citizens in building and sharing their own narratives about complex or long-term news stories. The first two design cycles of the platform have shown, by positive evidence, that this goal has been achieved, but in the previous papers no analysis was conducted regarding the social behavior of the participants in relation to consuming or being exposed to different viewpoints of their own. Several studies suggest that people crave more for reinforcement of opinion than they reject exposure from different points of view, and that from the standpoint of strengthening democratic discourse, hearing the other side is crucial. A key goal of this paper is to evaluate the extent to which the participants of the latest experiment in the aforementioned platform followed this behavior. Besides that, we aim to investigate whether the design of the platform favored the goal of encouraging the consumption of diverse sources and opinions, with an eye towards the next design cycles.

This article describes the microblogging as an ecosystem that allows sharing of ideas and connects people with similar interests. Analyzing microblogging dynamics we can detect users profile information, their main topics, interests increasing on them, and also interaction and networking. All these information can be used as a resource for team formation, informal learning and collaboration. We present a social matching model based on microblogging and social networks analysis, and also the prototype created to evaluate this proposal.

Presently, a plethora of internet service offerings imply in new emerging social behaviors. Learning by following an example is a behavior already investigated in educational theories which this study applies to internet based social networks. Matching people through their common knowledge is a form of leveraging knowledge acquisition by feeding complementary information across those clusters of similar peers. This paper presents a formalized mathematical model mapping a set of learning theories to knowledge interchange inside social networks. The model identifies the best peer matching to a given knowledge among people referring to bookmarked sites. A social matching engine was implemented and used to collect evidence on learning improvements that can achieved through knowledge based peer indications.

Team's composition of is key factor to its success, but differences between individual and collective interests result in obstacles for the configuration of teams. Considering only individuals skills is not enough to assemble a working group. We present Team Composer, an application based on Genetic Algorithm that implements a social matching strategy that combines skills, socio-emotional factors and roles to support the composition of teamwork.

Collaborative networks are composed by people from all over and all kind of background that meet to exchange common experience. The great challenge lies in finding significant, reliable information in view of the swelling volume of information output. Recommender systems emerged as a solution to reduce the problem of information overload. Collaborative filtering (CF) technique in particular automates the recommendation process by building on users' opinions of items in a community. However CF has limitations named cold start and first rater. The goal of this paper is to present a model using trust to minimize those problems.

Reputation systems represent an alternative to help users to create reliable relationships over communities of practice, allowing them to evaluate the individuals' actions and create their trust networks. ReCoP, a reputation model for communities of practice, was proposed based on the solutions found in virtual environments, in which trust on individuals is important for the setting up of interactions. In this paper, we describe a case study conducted to evaluate the estimative of reputation proposed at ReCoP - meta- evaluation mechanism.

Communities of practice (CoPs) encourage the exchange of experiences within organizations, but there is a risk involved in regard to the quality of participation. For this reason, it is important to provide a means for the community to be able to recognize the reliability and usefulness of the participation of individuals involved. This paper presents a proposal for the use of reputation systems in CoPs in order to assist users in creating relationships for honest and useful participation, based on trust, for the benefit of the entire community.

Work teams involved in intense work on knowledge deal with the difficulty of making decisions. They usually have to choose between several options. Work teams members have to evaluate these options by defining common evaluation criteria and analyzing information. This paper proposes the use of recommender systems for groups, based on fuzzy evaluations, to help teams make decisions

The process of creation of organization knowledge is a social process within which knowledge is shared among the members comprising an organization. Communities of practice have stood out as a way of promoting organizational learning, facilitating information and the knowledge exchange process among the members of an organization. Therefore, it is up to the companies to encourage their building and upkeep. This work presents the scoring strategy to encourage the participation of its members. A case study is presented, describing the application of the scoring mechanism to a community of practice, using work/learning cooperative/collaborative environment named TeamWorks

The communities of practice have been one of the ways adopted by organizations to promote the sharing of knowledge, the cooperative learning and consequently the improvement of working processes. However, one of its greatest problems is how to keep users interacting and exchanging relevant information to the whole community. Loyalty mechanisms used in marketing are proposed as a solution for this problem, aiming to stimulate the active participation of the members. A cooperative work environment, named TeamWorks is presented, illustrating the loyalty mechanism concerning documents recommendation.

Communities of practice have been adopted by many organizations to promote collaborative learning and sharing of knowledge. Although various applications have been built to support this learning process, they fail to deal with one of the main problem: keeping users interacting and exchanging relevant information. We claim that the supporting system should provide the necessary stimulus for the community members to collaborate. Therefore, we claim the recommendations as a type of loyalty mechanism. The design of the collaborative environment TeamWorks is presented, illustrating our proposal.

The efficiency of a meeting has a lot to do with the attitudes the participants have towards the meeting goals. The outcome of a meeting is very dependent on how the meeting participants behave, i.e., how they assume expected roles. We can select participants based on their ability to play the desired roles prior to the meeting, or we can later try to examine the participants' behavior by analyzing their interventions during the meeting. This paper discusses this latter approach and suggests a semantic network analysis to map interventions onto roles, defined as essential to a good team performance.