Exploring Smartphone Application Usage Logs with Declared Sociological Information

International audienceIn this paper we present an exploratory smartphone usage study with logs collected from users in the wild, combined with the sociodemographic, technological and cultural information provided by them. We observe a high diversity among users' most used applications, but by classifying applications into services we find significant correlations between service usage and socio-demographic profile. We discuss that sociological information has rich potential in characterizing smartphone usage and can be applied to interesting incentive strategies and use cases based on users' sociological context

Three-dimensional conversation : the shift to a public, asynchronous and persistent exchange in Malta

An observation of the evolution of the marketing messages of Telecommunications Company Vodafone between 2007 and 2013 sheds light on the significant changes that occurred in the communications arena throughout this period. The shift is not a hypothetical one; it is real and reflected in the shifting usage profiles of millions of mobile users. Moreover the shift is not limited to the changes in the technology which enables mediated conversation. Reference is made to existing literature to define the activity under study, understand the historical context of conversation, both in the mobile and online space, measure the present shifts and explore how findings can contribute to a better understanding of the future. In the context of the existing body of work and the significant changes that occurred over the past years, the research aims to propose a new model of conversation in response to the chosen research question, which asks, “how is conversation evolving as a result of take up of new media in Malta?“ A two‐step approach is adopted. The first research stream makes use of a data set of usage logs of a sample of smartphone adopters on the Vodafone network. A comparison of the usage logs before and after adoption is used to shed light on the influence of the device on the users’ conversations. The analysis is supported with two secondary experiments, one relating to the usage of mobile Internet on specific days during the year and the other extending the experiment to everyday conversation on Facebook. The second research stream consists of a review of the new media landscape with a specific focus on key themes. The findings are used to corroborate a model of shifting conversation. The model proposes that conversation is captured in three dimensions - a shift from synchronous to asynchronous conversation, from private to public and from transient to persistent exchanges

Can AI Mind Be Extended?

Andy Clark and David Chalmers’s theory of extended mind can be reevaluated in today’s world to include computational and Artificial Intelligence (AI) technology. This paper argues that AI can be an extension of human mind, and that if we agree that AI can have mind, it too can be extended. It goes on to explore the example of Ganbreeder, an image-making AI which utilizes human input to direct behavior. Ganbreeder represents one way in which AI extended mind could be achieved. The argument of this paper is that AI can utilize human input as a social extension of mind, allowing AI to access the external world that it would not otherwise be able to access

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations

Examining individual differences through ‘everyday’ smartphone behaviours: Exploring theories and methods.

The mass adoption of digital technologies has instigated a transition whereby people are no longer ‘independent organic actors’ in society but have amalgamated with the technology they use on a daily basis. Consequently, people leave behind a ‘digital fingerprint’ whenever they use technologies such as smartphones, and the qualities of this trace can predict a variety of characteristics about the user. In this thesis, I explore how individual differences such as personality, demographics, and health relate to directly observable smartphone behaviours, that are logged ‘in situ’ via software installed on the device itself. By adopting an interdisciplinary approach between psychology and computer science, this thesis primarily considers the theoretical (chapter two), ethical (chapter three) and methodological (chapter four) underpinnings required to explore these human-smartphone relationships. Notably, traces of use do not have to be complex, as meta-data such as the smartphone operating system a person uses can reveal information regarding a user’s personality, as long as there is trace-to-trait relevance. Findings from chapters five and six also reveal that some individual differences can be better predicted from objective smartphone use than others. For example, age and gender can be discerned from smartphone usage logs whereas, mental health variables only had small positive correlations with smartphone screen time. However, an important contribution of this thesis resides in its methodological considerations, as self-reports of technology use can impact the relationships with individual differences and cannot be used as a substitute for objective logs. All the above has applied implications for security and health, which can benefit from the ability to infer characteristics about people, when self-reports are arduous, unfeasible or lack scientific rigour

An Empirical Analysis of Internet Use on Smartphones: Characterizing Visit Patterns and User Differences

The original vision of ubiquitous computing was for computers to assist humans by providing subtle and fitting technologies in every environment. The iPhone and similar smartphones have provided continuous access to the internet to this end. In the current thesis, my goal was to characterize how the internet is used on smartphones to better understand what users do with technology away from the desktop. Naturalistic and longitudinal data were collected from iPhone users in the wild and analyzed to develop this understanding. Since there are two general ways to access the internet on smartphones—via native applications and a web browser—I describe usage patterns through each along with the influence of experience, the nature of the task and physical locations where smartphones were used on these patterns. The results reveal differences between technologies (the PC and the smartphone), platforms (native applications and the mobile browser), and users in how the internet was accessed. Findings indicate that longitudinal use of web browsers decreased sharply with time in favor of native application use, web page revisitation through browsers occurred very infrequently (approximately 25% of URLs are revisited by each user), bookmarks were used sparingly to access web content, physical location visitation followed patterns similar to virtual visitation on the internet, and Zipf distributions characterize mobile internet use. The web browser was not as central to smartphone use compared to the PC, but afforded certain types of activities such as searching and ad hoc browsing. In addition, users systematically differed from each other in how they accessed the internet suggesting different ways to support a wider spectrum of smartphone users

Measuring Large-Scale Social Networks with High Resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection