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As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research. As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research.

Browser extensions enrich users’ browsing experience, e.g., by blocking unwanted advertisements on websites. To perform these functions, users must grant certain permissions during the installation process. These permissions, however, give very limited information about the fact that they allow the extension to access user’s personal data and browsing behaviour, posing security and privacy risks. To understand users’ awareness of these privileges and the associated threats, we conducted an online survey with 353 participants, focusing on users’ attitude, knowledge, and preference towards extensions’ permission requests. We found that users report interest in seeking information, trust the developers but do little to protect their data. They have limited knowledge about the technical abilities of browser extensions and prefer per-mission statements that evoke a clear mental model. Based on our findings we derive recommendations for the improvement of browser extension permission dialogues through clear language, technical improvements and distinct responsibilities.

Passwords commonly contain personal information. However, there is limited awareness about its detrimental effect on the user’s online security. Current password meters do not take into account personal information and, therefore, their users are susceptible to targeted password guessing. In this paper, we present the MoiPrivacy password meter, that extends a neural network- and heuristic-based approach and considers a user’s personal information, while calculating the password strength and feedback. To do so, we analyzed the type of personal information used in passwords through an online survey (n = 62). We conducted a second user study (n = 49) for evaluating the MoiPrivacy browser extension. Our results show that MoiPrivacy significantly limits the inclusion of personal information in passwords.

Many applications of geotagged content are predicated on the concept of localness (e.g., local restaurant recommendation, mining social media for local perspectives on an issue). However, definitions of who is a "local" in a given area are typically informal and ad-hoc and, as a result, approaches for localness assessment that have been used in the past have not been formally validated. In this paper, we begin the process of addressing these gaps in the literature. Specifically, we (1) formalize definitions of "local" using themes identified in a 30-paper literature review, (2) develop the first ground truth localness dataset consisting of 132 Twitter users and 58,945 place-tagged tweets, and (3) use this dataset to evaluate existing localness assessment approaches. Our results provide important methodological guidance to the large body of research and practice that depends on the concept of localness and suggest means by which localness assessment can be improved.