Context Data Categories and Privacy Model for Mobile Data Collection Apps

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user's personality. As filling out personality questionnaires is tedious, we propose the prediction of the user's personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user's different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user's personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications.Comment: Accepted for publication at the 15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018

NotiMind: responses to smartphone notifications as affective sensors

Today's mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users’ emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users’ phones collected thousands of social and system notifications along with affect data collected via self-reported PANAS tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of Post and Remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different classifiers "in the wild" (F-measure 74-78% within-subject model, 72-76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication

Adolescents’ perceptions of digital media’s potential to elicit jealousy, conflict and monitoring behaviors within romantic relationships

Understanding the role of digital media in adolescents’ romantic relationships is essential to the prevention of digital dating violence. This study focuses on adolescents’ perceptions of the impact of digital media on jealousy, conflict, and control within their romantic relationships. Twelve focus group interviews were conducted, among 55 secondary school students (ngirls = 28; 51% girls) between the ages of 15 and 18 years (Mage = 16.60 years; SD age = 1.21), in the Dutch-speaking community of Belgium. The respondents identified several sources of jealousy within their romantic relationships, such as online pictures of the romantic partner with others and online messaging with others. Adolescents identified several ways in which romantic partners would react when experiencing feelings of jealousy, such as contacting the person they saw as a threat or looking up the other person’s social media profiles. Along with feelings of jealousy, respondents described several monitoring behaviors, such as reading each other’s e-mails or accessing each other’s social media accounts. Adolescents also articulated several ways that they curated their social media to avoid conflict and jealousy within their romantic relationships. For instance, they adapted their social media behavior by avoiding the posting of certain pictures, or by ceasing to comment on certain content of others. The discussion section includes suggestions for future research and implications for practice, such as the need to incorporate information about e-safety into sexual and relational education and the need to have discussions with adolescents, about healthy boundaries for communication within their friendships and romantic relationships.</jats:p

Take Control of Interruptions in Your Life: Lessons from Routine Activity Theory of Criminology

Steeped among the items on the dark side of information technology are personal technology interruptions. Past research has examined the negative impact of technology interruptions; however, the factors that are responsible for the increasing rate of interruptions are rarely discussed. In this study, by adapting the criminology theory of Routine Activity Theory (RAT), we propose three factors that lead to an interruption: number of interruption sources, absence of guardians, and individual targetness. Results from a survey of mobile users show that combinations of these factors have increased the interruption rate in our lives. Interestingly, just having more apps on the phones does not increase interruptions; it is a combination of the factors noted above

Patterns of multi-device use with the smartphone a video-ethnographic study of young adults’ multi-device use with smartphones in naturally occurring contexts

Using multiple devices at the same time is becoming increasingly common in the daily lives of users, be it for work or for leisure. This paper presents in situ qualitative and quantitative evidence of multi-device use from a dataset of over 200h of first-person and interview recordings (n = 41). We discuss three different ‘patterns’ of multi device use (work, leisure, mixed use) and illustrate the user experience in detail with three participant journeys. We find that the smartphone was always ‘in the mix’; we did not observe multi-device use without the smartphone, or isolated use of other devices. Overall, we suggest that looking at transitions between activities users engage in rather than devices they use is more effective to understand multi-device use. Based on this analysis, we highlight issues around the patterns and experiences of multi-device use in everyday life and provide recommendations for design and further research