Many individuals with autism manifest problems in transitioning to adulthood due to social impairments, communication difficulties, and rigid behaviors. One of those challenges is developing a robust and sufficiently large network of people who can provide advice about a variety of everyday situations. In this dissertation, I investigate ways of supporting adolescents with high functioning autism in navigating their everyday life through specializing social networking services (SNSs). A series of studies were conducted to identify opportunities for the design and use of a specialized SNS to foster the independence. The results demonstrates SNS can support some adolescents and adults with autism in increasing the independence by 1) facilitating the asking of a variety of online networks beyond a primary caregiver; and 2) allowing acquisition of rapid, direct, and informational advice with crowdsourcing. Drawing on several formative studies and investigations, I synthesized design guidelines for inquir.us, a specialized hybrid social question-and-answer (Q&A) platform with features for scaffolding question creation and crowdsourcing answers. Through the initial evaluation of inquir.us, I examined the Q&A behavior of individuals with autism on this platform and identified both opportunities and barriers to adoption in the context of supporting transition skills for the independence. The contributions of this thesis are: (1) a rich description of challenges and opportunities related to attaining independent life using SNSs; (2) empirical studies of individuals with autism’s online Q&A behavior; (3) design implications for designing a specialized SNS facilitating the Q&A interactions; and (4) the design and exploratory study of a social Q&A platform in the real world.

Many sources of rules govern our interactions with technology and our behavior online—law, ethical guidelines, community norms, website policies—and they do not always agree. This is particularly true in the context of content production because copyright law represents a collection of complex policies that often do not always account for the ways that people use and re-use digital media. Within legal gray areas, people make decisions every day about what is allowed, often negotiating multiple sources of rules. How do content creators make decisions about what they can and cannot do when faced with unclear rules, and how does the law (and perceptions of the law) impact technology use, creativity, and online interaction? Combining in-depth interviews, large-scale content analysis, and surveys, my work examines the complex relationship between law, site policy, norms, and technology. This dissertation provides a better understanding of how content creators engage with copyright and how norms organically form within communities of creators. It concludes with a set of design and policy recommendations for online community designers to help better support current practices among content creators.

Organizations are attempting to leverage their knowledge resources by integrating knowledge sharing systems, a key and new form of which are social computing tools. A large number of these initiatives fail, however, due to employees' reluctance to use, contribute content to, and share knowledge through such tools. Although research regarding one's motivation to share knowledge is extensive, there has been little research examining social computing systems, especially from the seeking and contributory perspectives—the two distinct, but closely interrelated facets of knowledge sharing. Motivated by such concerns, and by incorporating knowledge-seeking and knowledge- contribution perspectives in a single study, this research develops and empirically examines a theoretical model to explain what motivates employees to seek, contribute and share social tags using Enterprise Social Tagging Tools (ESTTs). Two research phases were employed to address the research objective. The goal of the first phase of the study was to explore factors affecting users’ tagging behavior in online social tagging tools. An extensive literature review was synthesized and a preliminary theoretical model emerged. A pilot study was conducted yielding 184 responses featuring eight different online social tagging tools. Mostly, the preliminary theoretical model showed positive influence on users’ tag behavior with a special focus on the newly developed concepts of information retrievability, information refindability. The goal of the study’s second phase was combining the results from the first phase with motivational theories to build and validate a belief-based and socio-organizational model that can explain employees’ tag seeking, contributing, and sharing behavior in ESTTs. The model was developed by employing theories such as Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and social exchange theory. Through a large-scale survey (n=481) in two large Information Technology (IT) companies, the model was validated. The results speak to the importance of the three newly developed factors impacting employees’ tag seeking, contributing and sharing behavior. These factors are uniquely context-specific reflecting actual features of social tagging tools and potentially social media in general. Particularly, the results reveal that employees' tag seeking behavior is affected by their perception of the ESTTs in terms of enjoyment, information retrievability, ease of use, and managerial influence. In the context of tag contribution and sharing, the results show that employees contribute and share tags because of their perception of information refindability, ease of use, altruism, and pro-sharing norms. Differences among the seeking, contributing and sharing model have implications for future research and practice. / The thesis investigates employees' motivation to participate in enterprise social tagging tools. It describes and validates a conceputal model composed of three types of motivations: technical, social, and organizational.

In order to support people's everyday health and wellness goals, health practitioners and organizations are embracing a more holistic approach to medicine---supporting patients both as individuals and members of their families and communities, and meeting people where they are: at home, work, and school. This 'everyday' approach to health has been enabled by new technologies, both dedicated-devices and services designed specifically for health sensing and feedback -- and multipurpose --such as smartphones and broadband-connected computers. Our physical relationship with computing has also become more intimate, and personal health devices can now track and report an unprecedented amount of information about our bodies, following their users around to an extent no doctor, coach or dietitian ever could. But we still have much to learn about how pervasive health devices can actually help promote the adoption of new health practices in daily life. Once they're `in the wild,' such devices interact with their users, but also the physical, social and political worlds in which those users live. These external factors---such as the walkablity of a person's neighborhood or the social acceptability of exercise and fitness activities---play a significant role in people's ability to change their health behaviors and sustain that change. Specifically, social theories of behavior change suggest that peer support may be critical in changing health attitudes and behaviors. These theories---Social Support Theory, Social Cognitive Theory and Social Comparison Theory among them---offer both larger frameworks for understanding the social influences of health behavior change and specific mechanisms by which that behavior change could be supported through interpersonal interaction. However, we are only beginning to understand the role that pervasive health technologies can play in supporting and mediating social interaction to motivate people's exploration and adoption of healthy behaviors. In this dissertation I seek to better understand how social computing technologies can help people help each other live healthier lives. I ground my research in a participant-led investigation of a specific population and condition: adolescents and obesity prevention. I want to understand how social behavior change theories from psychology and sociology apply to pervasive social health technology. Which mechanisms work and why? How does introducing a pervasive social health system into a community affect individuals' behaviors and attitudes towards their health? Finally, I want to contribute back to those theories, testing their effectiveness in novel technologically mediated situations. Adolescent obesity is a particularly salient domain in which to study these issues. In the last 30 years, adolescent obesity rates in the US alone have tripled, and although they have leveled off in recent years they remain elevated compared to historical norms. Habits formed during adolescence can have lifelong effects, and health promotion research shows that even the simple act of walking more each day has lasting benefits. Everyday health and fitness research in HCI has generally focused on social comparison and "gamified" competition. This is especially true in studies focused on adolescents and teens. However, both theory from social psychology and evidence from the health promotion community suggest that these direct egocentric models of behavior change may be limited in scope: they may only work for certain kinds of people, and their effects may be short-lived once the competitive framework is removed. I see an opportunity for a different approach: social tools for everyday adolescent health. These systems, embedded in existing school and community practices, can leverage scalable, non-competitive social interaction to catalyze positive perceptions of physical activity and social support for fitness, while remaining grounded in the local environment. Over the last several years I have completed a series of field engagements with middle school students in the Atlanta area. I have focused on students in a majority-minority low-income community in the Atlanta metropolitan area facing above-average adult obesity levels, and I have involved the students as informants throughout the design process. In this dissertation, I report findings based on a series of participatory design-based formative explorations; the iterative design of a pedometer-based pervasive health system to test these theories in practice; and the deployment of this system---StepStream---in three configurations: a prototype deployment, a `self-tracking' deployment, and a `social' deployment. In this dissertation, I test the following thesis: A school-based social fitness approach to everyday adolescent health can positively influence offline health behaviors in real-world settings. Furthermore, a noncompetitive social fitness system can perform comparably in attitude and behavior change to more competitive or direct-comparison systems, especially for those most in need of behavior change}. I make the following contributions: (1) The identification of tensions and priorities for the design of everyday health systems for adolescents; (2) A design overview of StepStream, a social tool for everyday adolescent health; (3) A description of StepStream's deployment from a socio-technical perspective, describing the intervention as a school-based pervasive computing system; (4) An empirical study of a noncompetitive awareness system for physical activity; (5) A comparison of this system in two configurations in two different middle schools; (6) An analysis of observational learning and collective efficacy in a pervasive health system.

Online networks of individuals have been used to solve a number of problems in a scale that would not be possible if not within a connected, virtual and social environment such as the internet. However, the quality of solutions provided by individuals of an online network can vary significantly thus making work quality unreliable. This dissertation investigates factors that can influence the quality of the work output of individuals in online social networks. Specifically, we show that when solving tasks with small duration (under 5 minutes), also known as microtasks, individuals decision making will be strongly biased by costs of searching (and evaluating) options rather than financial or non-financial incentives. Indeed, we are able to show that we can influence individuals decisions, when solving problems, by rearranging elements visually to modify an the search sequence of an individual, be it by designing the virtual work environment or manipulating which options are first shown in non-controlled environments such as the Amazon Mechanical Turk labor market. We performed several experiments in online networks where individuals are invited to work on tasks with varying degrees of difficulty within three settings: mathematical games with objective truth (Sudoku and SAT instances), surveys with subjective evaluation (public policy polling) and labor markets (Amazon Mechanical Turk). We show that the time spent solving problems and the user interface are more relevant to the quality of work output than previous research have assumed and that individuals do not change this behavior while solving the sets of problems. Finally, to complement our study of online problem-solving, we present additional experiments in an online labor market (Amazon Mechanical Turk) that agrees with our networked experiments, shedding new light on how and why people solve problems.

Online networks of individuals have been used to solve a number of problems in a scale that would not be possible if not within a connected, virtual and social environment such as the internet. However, the quality of solutions provided by individuals of an online network can vary significantly thus making work quality unreliable. This dissertation investigates factors that can influence the quality of the work output of individuals in online social networks. Specifically, we show that when solving tasks with small duration (under 5 minutes), also known as microtasks, individuals decision making will be strongly biased by costs of searching (and evaluating) options rather than financial or non-financial incentives. Indeed, we are able to show that we can influence individuals decisions, when solving problems, by rearranging elements visually to modify an the search sequence of an individual, be it by designing the virtual work environment or manipulating which options are first shown in non-controlled environments such as the Amazon Mechanical Turk labor market. We performed several experiments in online networks where individuals are invited to work on tasks with varying degrees of difficulty within three settings: mathematical games with objective truth (Sudoku and SAT instances), surveys with subjective evaluation (public policy polling) and labor markets (Amazon Mechanical Turk). We show that the time spent solving problems and the user interface are more relevant to the quality of work output than previous research have assumed and that individuals do not change this behavior while solving the sets of problems. Finally, to complement our study of online problem-solving, we present additional experiments in an online labor market (Amazon Mechanical Turk) that agrees with our networked experiments, shedding new light on how and why people solve problems.

Metódos analíticos de investigação são usualmente ineficazes para sistemas computacionais sociais já que apenas algumas iterações do sistema já são suficientes para que o sistema se torne imprevisível. Portanto, uma das principais questões na Computação Social é o desenvolvimento de modelos sociais passíveis de investigação. Assim é possível que se compreenda o relacionamento complexo entre os componentes de sistemas sociais computacionais e o resultado. Este aspectos incluem a modelagem, a estrutura de comunicação e características individuais do agentes envolvidos na resolução dos problemas. do processo social. Esta tese explora sistemas computacionais de resolução de problemas com foco em sistemas artificiais e heterogêneos. Nela é feita uma compilação extensiva da literatura relacionada em sistemas complexos onde as contribuições do candidato são expostas dentro de contextos específicos da área. Entre elas está o estudo de modelos abstratos e gerais de resolução social de problemas, a investigação do impacto da centralidade no resultado individual e coletivo, a análise experimental de modelos heterogêneos de resolução social de problemas. Quando integradas, estas contribuições reforçam o entendimento sobre a importância da rede e das estruturas de comunicação, a composição estratégica do sistema, a estrutura do problema e possíveis padrões gerais na resolução social de problemas. / This thesis reviews and investigates social problem-solving with a particular focus on artificial and heterogeneous systems. More specifically, we not only compile and comprehensively examine recent research results, but also discuss future directions in the study of such heterogeneous complex systems. Given their complex nature, such systems often defy analyses. Even computationally simple models can behave unpredictably after a few iterations. Therefore, one central issue in Social Computing is to devise models of social interaction that are amenable to investigation. This way, one can understand the complex relationships among the components and the outcome of the social process. This thesis surveys scientific inquiries concerned with fundamental aspects in social problemsolving systems and their impact in ability and performance of such systems. These aspects include modeling, communication structure and individual problem-solver traits. This thesis also reports the student endeavour during the period of research and summarizes several already published contributions. Among them there is (i) the study of general frameworks for the study of social problem-solving, (ii) the investigation of the role of centrality in individual and collective outcomes, and (iii) the exploration of heterogeneous models of social problem-solving. These three points, in an integrated perspective underpin the understanding of network and communication structures, adjust the strategic systems’ composition, and exploit problems’ structures and patterns in social problemsolving systems.

Nossa incapacidade em compreender todos os fatores responsáveis por fenômenos naturais faz com que tenhamos que recorrer a simplificações na representação e na explicação destes. Por sua vez, a forma com que representamos e pensamos a respeito destes fenômenos é influenciada por fatores de natureza interna, como o nosso estado psicológico, ou então de natureza externa, como o ambiente social. Dentre os fatores externos, o ambiente social, ou contexto social, é um dos que tem maior influência na forma que pensamos e agimos. Quando estamos em grupo, mudamos a todo instante a forma com que resolvemos problemas em resposta ao contexto que nos cerca. Entretanto, esta característica até então foi pouco explorada em modelos computacionais de resolução coletiva de problemas. Este trabalho investiga o impacto do contexto social na resolução coletiva de problemas. Nós apresentaremos evidências de que o contexto social tem um papel importante na forma com que o grupo e o indivíduos se comportam. Mais precisamente, nós mostraremos que a centralidade de um indivíduo na rede social nem sempre é um bom preditor de sua contribuição quando o mesmo pode adaptar sua estratégia de busca em resposta ao contexto. Além disso, mostraremos que a adaptação ao contexto social por parte dos indivíduos pode melhorar o desempenho coletivo, facilitando a convergência para soluções boas; e que a diversidade de estratégias de resolução do problema não leva necessariamente a uma diversidade de soluções na população; e que, mesmo que o contexto social seja percebido da mesma forma pelos indivíduos, a forma com que eles reagem pode levar a diferentes resultados. Todos estes resultados suportam a ideia de que o contexto social deve ser considerado em experimentos com resolução social de problemas. Por fim, concluímos o trabalho discutindo o impactso do mesmo e apontando novos problemas a serem investigados. / Our inability to perceive and understand all the factors that account for real-world phenomena forces us to rely on clues when reasoning and making decisions about the world. Clues can be internal such as our psychological state and our motivations; or external, such as the resources available, the physical environment, the social environment, etc. The social environment, or social context, encompasses the set of relationships and cultural settings by which we interact and function in a society. Much of our thinking is influenced by the social environment and we constantly change the way we solve problems in response to our social environment. Nevertheless, this human trait has not been thoughtfully investigated by current computational models of human social problem-solving, for these models have lacked the heterogeneity and self-adaptive behavior observed in humans. In this work, we address this issue by investigating the impact of social context in social problem solving by means of extensive numerical simulations using a modified social model. We show evidences that social context plays a key role in how the system behaves and performs. More precisely, we show that the centrality of an agent in the network is an unreliable predictor the agent’s contribution when this agent can change its problem-solving strategy according to social context. Another finding is that social context information can be used to improve the convergence speed of the group to good solutions and that diversity in search strategies does not necessarily translates into diversity in solutions. We also determine that even if nodes perceive social context in same way, the way they react to it may lead to different outcomes along the search process. Together, these results contribute to the understanding that social context does indeed impact in social problem-solving. We conclude discussing the overall impact of this work and pointing future directions.

Sistemas sociais têm se tornado cada vez mais relevantes para a Ciência da Computação em geral e para a Inteligência Artificial em particular. Tal interesse iniciou-se pela necessidade de analisar-se sistemas baseados em agentes onde a interação social destes agentes pode ter um impacto no resultado esperado. Uma tendência mais recente vem da área de Processamento Social de Informações, Computação Social e outros métodos crowdsourced, que são caracterizados por sistemas de computação compostos de pessoas reais, com um forte componente social na interação entre estas. O conjunto de todas interações sociais e os atores envolvidos compõem uma rede social, que pode ter uma forte influência em o quão eficaz ou eficiente o sistema pode ser. Nesta tese, exploramos o papel de estruturas de redes em sistemas sociais que visam a solução de problemas. Enquadramos a solução de problemas como uma busca por soluções válidas em um espaço de estados e propomos um modelo - a Rede Memética - que é capaz de realizar busca utilizando troca de informações (memes) entre atores interagindo em uma rede social. Tal modelo é aplicado a uma variedade de cenários e mostramos como a presença da rede social pode melhorar a capacidade do sistema em encontrar soluções. Adicionalmente, relacionamos propriedades específicas de diversas redes bem conhecidas ao comportamento observado para os algoritmos propostos, resultando em um conjunto de regras gerais que podem melhorar o desempenho de tais sistemas sociais. Por fim, mostramos que os algoritmos propostos são competitivos com técnicas tradicionais de busca heurística em diversos cenários. / Social systems are increasingly relevant to computer science in general and artificial intelligence in particular. Such interest was first sparkled by agent-based systems where the social interaction of such agents can be relevant to the outcome produced. A more recent trend comes from the general area of Social Information Processing, Social Computing and other crowdsourced systems, which are characterized by computing systems composed of people and strong social interactions between them. The set of all social interactions and actors compose a social network, which may have strong influence on how effective the system can be. In this thesis, we explore the role of network structure in social systems aiming at solving problems, focusing on numerical and combinatorial optimization. We frame problem solving as a search for valid solutions in a state space and propose a model - the Memetic Network - that is able to perform search by using the exchange of information, named memes, between actors interacting in a social network. Such model is applied to a variety of scenarios and we show that the presence of a social network greatly improves the system capacity to find good solutions. In addition, we relate specific properties of many well-known networks to the behavior displayed by the proposed algorithms, resulting in a set of general rules that may improve the performance of such social systems. Finally, we show that the proposed algorithms can be competitive with traditional heuristic search algorithms in a number of scenarios.

Sistemas sociais têm se tornado cada vez mais relevantes para a Ciência da Computação em geral e para a Inteligência Artificial em particular. Tal interesse iniciou-se pela necessidade de analisar-se sistemas baseados em agentes onde a interação social destes agentes pode ter um impacto no resultado esperado. Uma tendência mais recente vem da área de Processamento Social de Informações, Computação Social e outros métodos crowdsourced, que são caracterizados por sistemas de computação compostos de pessoas reais, com um forte componente social na interação entre estas. O conjunto de todas interações sociais e os atores envolvidos compõem uma rede social, que pode ter uma forte influência em o quão eficaz ou eficiente o sistema pode ser. Nesta tese, exploramos o papel de estruturas de redes em sistemas sociais que visam a solução de problemas. Enquadramos a solução de problemas como uma busca por soluções válidas em um espaço de estados e propomos um modelo - a Rede Memética - que é capaz de realizar busca utilizando troca de informações (memes) entre atores interagindo em uma rede social. Tal modelo é aplicado a uma variedade de cenários e mostramos como a presença da rede social pode melhorar a capacidade do sistema em encontrar soluções. Adicionalmente, relacionamos propriedades específicas de diversas redes bem conhecidas ao comportamento observado para os algoritmos propostos, resultando em um conjunto de regras gerais que podem melhorar o desempenho de tais sistemas sociais. Por fim, mostramos que os algoritmos propostos são competitivos com técnicas tradicionais de busca heurística em diversos cenários. / Social systems are increasingly relevant to computer science in general and artificial intelligence in particular. Such interest was first sparkled by agent-based systems where the social interaction of such agents can be relevant to the outcome produced. A more recent trend comes from the general area of Social Information Processing, Social Computing and other crowdsourced systems, which are characterized by computing systems composed of people and strong social interactions between them. The set of all social interactions and actors compose a social network, which may have strong influence on how effective the system can be. In this thesis, we explore the role of network structure in social systems aiming at solving problems, focusing on numerical and combinatorial optimization. We frame problem solving as a search for valid solutions in a state space and propose a model - the Memetic Network - that is able to perform search by using the exchange of information, named memes, between actors interacting in a social network. Such model is applied to a variety of scenarios and we show that the presence of a social network greatly improves the system capacity to find good solutions. In addition, we relate specific properties of many well-known networks to the behavior displayed by the proposed algorithms, resulting in a set of general rules that may improve the performance of such social systems. Finally, we show that the proposed algorithms can be competitive with traditional heuristic search algorithms in a number of scenarios.