Today’s context-aware mobile phones allow developers to build intelligent and adaptive applications. The data demand induced by context awareness leads to decreased trust and increased privacy concerns. However, users’ deeper reasons and real-world fears that underlie these concerns are not fully understood. We conducted an online survey (N=100) and semi-structured interviews (N=20) to understand users’ concerns about smartphone data privacy. We investigated three key areas: general user understanding and misconceptions, specific in-depth concerns, and mitigation strategies. We found that effective transparency and control are the central themes across all areas. Users are concerned about privacy issues negatively impacting their lives, especially through financial loss, physical harm, or manipulation. We show that privacy measures should be implemented with a stronger focus on the user by keeping the user in the loop through transparency and control.

Rich user information is gained through user tracking and power mobile smartphone applications. Apps thereby become aware of the user and their context, enabling intelligent and adaptive applications. However, such data poses severe privacy risks. Although users are only partially aware of them, awareness increases with the proliferation of privacy-enhancing technologies. How privacy literacy and raising privacy concerns affect app adoption is unclear; however, we hypothesize that it leads to a lower adoption rate of data-heavy smartphone apps, as non-usage often is the user's only option to protect themselves. We conducted a survey (N=100) to investigate the relationship between privacy-relevant app- and publisher characteristics with the users' intention to install and use it. We found that users are especially critical of contentful data types and apps with rights to perform actions on their behalf. On the other hand, the expectation of a productive benefit induced by the app can increase the app-adoption intention. Our findings show which aspects designers of privacy-enhancing technologies should focus on to meet the demand for more user-centered privacy.

Today, users are constrained by binary choices when configuring permissions. These binary choices contrast with the complex data collected, limiting user control and transparency. For instance, weather applications do not need exact user locations when merely inquiring about local weather conditions. We envision sliders to empower users to fine-tune permissions. First, we ran two online surveys (N=123 & N=109) and a workshop (N=5) to develop the initial design of Privacy Slider. After the implementation phase, we evaluated our functional prototype using a lab study (N=32). The results show that our slider design for permission control outperforms today's system concerning all measures, including control and transparency.

Smartphones provide various functions supporting users in their daily lives. However, the temptation of getting distracted and tuning out is high leading to so-called rabbit holes. To quantify rabbit hole behavior, we developed an Android tracking application that collects smartphone usage enriched with experience sampling questionnaires. We analyzed 14,395 smartphone use sessions from 21 participants, collected over two weeks, showing that rabbit hole sessions are significantly longer and contain more user interaction, revealing a certain level of restlessness in use. The context of rabbit hole sessions and subjective results revealed different triggers for spending more time on the phone. Next, we conduct an expert focus group (N=6) to put the gained insights into perspective and formulate a definition of the mobile phone rabbit hole. Our results form the foundation for predicting and communicating the mobile phone rabbit hole, especially when prolonged smartphone use results in regret.

Today, adaptive mobile applications use mobile sensing and user tracking, allowing for adaptation to the users' context and needs. This raises several privacy concerns. Privacy dashboards provide transparency and sharing control; however, their impact on the users' behavior is unclear. To shed light on the effects of (a) transparency and (b) control features, we developed a mobile sensing privacy dashboard and evaluated it in the wild (N=227). We found that the pure presentation of raw logging data is rather deterring, and users tend to use the app less, but offering the user control over the data collection can compensate for that. Users used the control features rarely and, as such, did not affect the data collocation. Our work informs the design of future privacy-enhancing interfaces in applications relying on passively collected mobile sensing data. Moreover, our results encourage the adoption of privacy dashboards in the applications and relieve developers from concerns about the negative influences of transparency information on the quality of collected data.

Internet memes are (multi)media pieces, found all across the world-wide-web. Often disposing of a humorous component, they express and reflect on all kinds of local and global phenomena. Within our work, we explore how people can use internet memes to express and reflect on themselves. We built MEMEory, a mobile meme journaling app. We evaluated the prospect of meme journaling, nicknamed ”memeing”, alongside a written diary in a 2-week field study with 31 participants. Opposed to more neutral chronicle-style text entries, our results suggest that participants used memes to express specific single, rather negative events and emotions throughout the day. When reflecting on daily events, the contained emotional and often humorous connotation of memes helped participants view negative events as more positive in retrospect. Although more difficult, memeing was perceived as significantly more motivating and enjoyable. Qualitative insights show that memeing can present a fun, engaging, expressive and memorable journaling experience.

Common sources of career information like websites often provide a static overall picture of a job, yet lack personal insights into the daily working life. To address this problem, we present a novel mobile career guidance method: It enables users to remotely gain an impression of different work routines by receiving several short, scheduled chat messages from a persona throughout the day. These messages were previously collected from real professionals reporting on their tasks over a week. We implemented a smartphone application to compare our message-based approach to a traditional blog entry in a two-week within-subject field study (N = 17). Users highlighted that the scheduled messages (1) enhanced their understanding of work routines by integrating career information into their own daily context and (2) offered authentic insights into the jobs. We discuss design implications for mobile career guidance systems and future opportunities for presenting chunks of information in a temporal context.

Traces of use in public environments show the behaviour patterns of the masses. Taking advantage of this quality, we want to use such traces as design tool to indicate possible interactions in e.g. newly built areas while keeping a natural and calm environment. Due to current lacking knowledge about such traces, this work aims at understanding the perception of traces of use in public places. Therefore we collected a total of 182 pictures of traces of use in urban environments. A focus group discussed and classified a preselected set of pictures. In an online picture viewing survey, 18 different pictures were reviewed for pattern identification (N= 32-52). Overlaps were visualized in heatmaps. We contribute an analysis of which public traces of use are easy to recognize with great agreement and which are not.

We present a concept and tool for studying language use in everyday mobile text communication (e.g. chats). Our approach for the first time enables researchers to collect comprehensive data on language use during unconstrained natural typing (i.e. no study tasks) without logging readable messages to preserve privacy. We achieve this with a combination of three customisable text abstraction methods that run directly on participants' phones. We report on our implementation as an Android keyboard app and two evaluations: First, we simulate text reconstruction attempts on a large text corpus to inform conditions for minimising privacy risks. Second, we assess people's experiences in a two-week field deployment (N=20). We release our app as an open source project to the community to facilitate research on open questions in HCI, Linguistics and Psychology. We conclude with concrete ideas for future studies in these areas.