Julia M. Mayer

Lessons learned from participatory design with and for people with dementia

In this paper we describe challenges and lessons learned from developing a mobile touch screen based assistive tool for people with dementia. We focus not on features of the tool but the general participatory design process that was applied. Insights presented are gained from interviews, focus groups and observations. We found that projecting problems on imaginary characters and using simple games specifically customized for people with dementia ease the process of eliciting user needs and realistic prototypes allow design evaluations with people with dementia early on.

Making Social Matching Context-Aware: Design Concepts and Open Challenges

Social matching systems recommend people to people. In an ideal world, such systems could be context-aware, in that they would introduce users to each other in situations where they are mutually interested, available and open to meeting (i.e., facilitate a valuable encounter). Unfortunately, todays systems primarily match individuals based on simple similarity and proximity metrics. This paper explores how contextual information available on todays mobile phones could be used to identify opportunities for people to make valuable new connections. Three types of context that are relevant for this work are: relational, social and personal. We present insights gained from several iterations of semi-structured interviewing (N=58) exploring these three types of contexts and propose novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context.

Identifying Opportunities for Valuable Encounters: Toward Context-Aware Social Matching Systems

Mobile social matching systems have the potential to transform the way we make new social ties, but only if we are able to overcome the many challenges that exist as to how systems can utilize contextual data to recommend interesting and relevant people to users and facilitate valuable encounters between strangers. This article outlines how context and mobility influence peoples motivations to meet new people and presents innovative design concepts for mediating mobile encounters through context-aware social matching systems. Findings from two studies are presented. The first, a survey study (n = 117) explored the concept of contextual rarity of shared user attributes as a measure to improve desirability in mobile social matches. The second, an interview study (n = 58) explored peoples motivations to meet others in various contexts. From these studies we derived a set of novel context-aware social matching concepts, including contextual sociability and familiarity as an indicator of opportune social context; contextual engagement as an indicator of opportune personal context; and contextual rarity, oddity, and activity partnering as an indicator of opportune relational context. The findings of these studies establish the importance of different contextual factors and frame the design space of context-aware social matching systems.

Supporting Opportunities for Context-Aware Social Matching: An Experience Sampling Study

Mobile social matching systems aim to bring people together in the physical world by recommending people nearby to each other. Going beyond simple similarity and proximity matching mechanisms, we explore a proposed framework of relational, social and personal context as predictors of match opportunities to map out the design space of opportunistic social matching systems. We contribute insights gained from a study combining Experience Sampling Method (ESM) with 85 students of a U.S. university and interviews with 15 of these participants. A generalized linear mixed model analysis (n=1704) showed that personal context (mood and busyness) as well as sociability of others nearby are the strongest predictors of contextual match interest. Participant interviews suggest operationalizing relational context using social network rarity and discoverable rarity, and incorporating skill level and learning/teaching needs for activity partnering. Based on these findings we propose passive context-awareness for opportunistic social matching.

The doing of doing stuff: understanding the coordination of social group-activities

This paper explores how the adoption of mobile and social computing technologies has impacted upon the way in which we coordinate social group-activities. We present a diary study of 36 individuals that provides an overview of how group coordination is currently performed as well as the challenges people face. Our findings highlight that people primarily use open-channel communication tools (e.g., text messaging, phone calls, email) to coordinate because the alternatives are seen as either disrupting or curbing to the natural conversational processes. Yet the use of open-channel tools often results in conversational overload and a significant disparity of work between coordinating individuals. This in turn often leads to a sense of frustration and confusion about coordination details. We discuss how the findings argue for a significant shift in our thinking about the design of coordination support systems.

Towards an understanding of social inference opportunities in social computing

Social computing applications are transforming the way we make new social ties, work, learn and play, thus becoming an essential part our social fabric. As a result, people and systems routinely make inferences about peoples personal information based on their disclosed personal information. Despite the significance of this phenomenon the opportunity to make social inferences about users and how this process can be managed is poorly understood. In this paper we 1) outline why social inferences are important to study in the context of social computing applications, 2) how we can model, understand and predict social inference opportunities 3) highlight the need for social inference management systems, and 4) discuss the design space and associated research challenges. Collectively, this paper provides the first systematic overview for social inference research in the area of social computing.

Common attributes in an unusual context: predicting the desirability of a social match

Social matching systems recommend people to other people. With the widespread adoption of smartphones, mobile social matching systems could potentially transform our social landscape. However, we have a limited understanding of what makes a good social match in the mobile context. We present a theoretical framework which outlines how a users context and the rarity of different affinity measures in various contexts (match rarity) can be used to provide valuable social matches. We suggest that if a user attribute is very rare in a particular context, users will generally be more interested in an affinity match. We conducted a survey study to assess this framework with 117 respondents. We found that both context and match rarity significantly influence interest in a social match. These results validate the key aspects of the framework. We discuss the results in terms of implications for social matching system design.

Is there a place for serendipitous introductions

Mobile social matching systems have the potential to transform the way we make new social ties. Yet, there are many challenges as to how systems could utilize contextual data to support serendipitous introductions between strangers. I investigate how contextual factors influence peoples motivations to meet new people and how opportunistic social matching system design can benefit from concepts like contextual rarity, oddity and sociability. My research will result in an enhanced understanding of peoples context-dependent motivations to meet new people and will contribute a theoretical model that predicts contextually-relevant match opportunities and innovative design affordances for opportunistic social matching systems.

How did you know that about me? Protecting users against unwanted inferences

The widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications’ ability to support social action, their use puts individual privacy at considerable risk. This is in large part due to the fact that the public sharing of personal information through social computing applications enables potentially unwanted inferences about users’ identity, location, or other related personal information. This paper provides a systematic overview of the social inference problem. It highlights the public’s and research community’s general lack of awareness of the problem and associated risks to user privacy. A social inference risk prediction framework is presented and associated empirical studies that attest to its validity. This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems. Challenges examined include how to address the computational complexity of social inference risk modeling and designing user interfaces that inform users about social inference opportunities.