How do people use their smartphone? A data scientific approach to describe and identify user-related, system-related and context-related patterns in use

Quantifying and understanding the myriad ways people use their phones and how that impacts their relationships, cognitive abilities, mental health, and well being is increasingly important in our phone-centric society. However, most studies on the patterns of phone use have focused on theory-driven tests of specific usage hypotheses using self-report questionnaires or analyses of smaller datasets. In this work we present a series of analyses from a large corpus of over 3000 users that combine data-driven and theory-driven analyses to identify reliable smartphone usage patterns and clusters of similar users. Furthermore, we compare the stability of user clusters across userand system-initiated sessions, as well as during the hypothesized ritualized behavior times directly before and after sleeping. Our results indicate support for some hypothesized usage patterns but present a more complete and nuanced view of how people use smartphones. Introduction To date, no technology has diffused into society more rapidly and globally than the smartphone (ITU, 2017; Poushter & Stewart, 2016). The success of the smartphone can be attributed to its substantial contribution to our daily autonomy. Smartphones enable us to perform our social roles, manage our social networks and access personalized information and services independent from time and place constraints (Vanden Abeele, De Wolf, & Ling, 2018). While this ubiquitous connectivity brings new opportunities, it also poses new risks and challenges. Smartphone use is, for example, linked to mental health problems (e.g., Jenaro, Flores, Gómez-Vela, González-Gil, & Caballo, 2009; Twenge, Joiner, Rogers, & Martin, 2018), stress (Hsiao, Shu, & Huang, 2017), reduced productivity (e.g., David, Kim, Brickman, Ran, & Curtis, 2015; Spira & Feintuch, 2005; Vanden Abeele, Schouten, et al., 2016), poor sleep quality (Lanaj, Johnson, & Barnes, 2014), conflict in personal relationships (Vanden Abeele, Antheunis, & Schouten, 2016), poor work-life balance (Wajcman, Bittman, & Brown, 2008) and risky behaviors such as texting while driving (Bayer & Campbell, 2012). Given the promises and perils of smartphone use, it is not surprising that media effects researchers have embraced the study of the effects of smartphone use. A majority of the extant research on the implications of smartphone use has used survey designs to identify how people’s smartphone usage is related to their thoughts, emotions and behaviors. These survey studies typically involve theory-driven tests of specific usage hypotheses, using questionnaires in which users self-report on their smartphone use. These studies have been valuable; they have identified relationships between smartphone use and a myriad of antecedents and outcomes, thus contributing to theory formation and to societal valorization. Despite their value, however, they have important limitations. A first limitation is that studies may present inaccurate or even invalid findings by including self-report measures of smartphone use that are known to be unreliable in their analysis; there is a large discrepancy between how frequently people say they use their smartphone, and what they actually do. The reason why self-report measures of smartphone use are unreliable is that the fragmented, yet frequent occurrence of the behavior is difficult for individuals to recall accurately. Hence, individuals instead rely on heuristic estimation strategies when self-reporting their phone use and these estimation strategies are biased. For example, research shows that heavy phone users underestimate their use, whereas light phone users overestimate their use (Vanden Abeele, Beullens, & Roe, 2013). A second limitation of relying on self-reported smartphone use is that several studies limit themselves to operationalizations of smartphone use in terms of self-reported frequency and/or duration of use (e.g., Bayer & Campbell, 2012). However, smartphone use is fragmented and patterned behavior (Deng et al., 2018; Oulasvirta, Rattenbury, Ma, & Raita, 2011). It is this very nature of smartphone use that may produce unique effects; we should thus explore which patterns there are, and in which way they are meaningful. Scholars currently circumvent this issue by asking individuals to self-report about their patterned behavior (e.g., to indicate how habitual their phone use is), but we lack insight into the reliability of these assessments; and there is a risk that factors such as social desirability bias these responses. Third, a limitation of survey designs is that they typically focus on smartphone use as a stable behavior that only shows variability across individuals, as within-person variability is difficult to assess via cross-sectional self-report measurements; this is unfortunate, as such intra-person variability may be meaningful. For example, specific patterns of smartphone behavior may vary depending on person-specific factors such as a person’s mood or cognitive state. Moreover, self-reported smartphone use measures fail to account for variability that is system-specific or context-specific. With respect to system-specific variability, smartphone use may be associated with appor device settings, for example, smartphone behaviors that manifest themselves in response to incoming notifications. With respect to contextspecificity, smartphone use may be associated with a myriad of situational characteristics, such as those that pertain to the spatial, temporal or social environment in which the smartphone use takes place. For example, smartphone checking behaviors may manifest differently in situational contexts where the behavior is deemed inappropriate (e.g., while driving or during a romantic dinner) than where it is deemed appropriate (e.g., during waiting situations). However, most of the extant empirical work overlooks differences within the individual, which may, in turn, result in part from differences in device-generated events (e.g., did usage start in response to a notification or not) or differences in contexts (e.g., the time or place of use). In recent years, scholars have begun to addresses the above shortcomings by exploring the use of smartphone logging data rather than self-reports in empirical studies. In addition to capturing what people do rather than what they say, smartphone logging data reveal the dynamic nature of smartphone use, in the form of within-person variability in smartphone use. Moreover, the log data can include system-generated smartphone cues and can contextualize the smartphone use if indicators of the spatio-temporal context are registered. Gathering log data results in a rich collection of time-dependent signals that can be made sense of by relying on computational research methods (e.g., Deng et al., 2018; Gouin-Vallerand & Mezghani, 2014; Hao, Wang & Xu, 2016). The use of computational methods to study the effects of smartphone use is promising, as it creates opportunities to explore complex systemand context-specific patterns of smartphone usage, both at the aggregate level, and at the level of one unique individual. To date, studies presenting a computational analysis of smartphone log data are scarce in the field of communication sciences, and particularly that of smartphone effects research. Most of the extant work has focused on research questions in the area of product design (e.g., Wu, Liang & Tang, 2017) or computer science (e.g., Gouin-Vallerand & Mezghani, 2014). Moreover, these studies mostly rely on datasets garnered among relatively small sets of individuals (e.g., Hao, Wang & Xu, 2016). As a result, many meaningful questions about patterns in smartphone use and their social implications remain unexplored. This is unfortunate, as these patterns lie at the heart of research questions such as how fragmentation in smartphone use is linked to attention in various spheres of our everyday life (e.g., Williams, 2018). A reason why the use of smartphone log data is rare in the field of communication research may be that we currently have only a limited understanding of what patterns could potentially be distilled from log data using computational methods. Additionally, scholars may benefit from gaining access to low-threshold operational tools to subtract such patterns from log data. Hence, the purpose of this study is to provide an analysis of three patterns that can be distilled by drawing sets of data-driven features from smartphone log data. The patterns that we aim to identify are (1) app repertoire, (2) habitual smartphone use, and (3) fragmentation. The app repertoire concept can be understood as an element of niche theory (Dimmick, Kline, & Stafford, 2016; Ramirez, Dimmick, Feaster, & Lin, 2008). The central assumption of niche theory is that media need to offer unique benefits to their users in order to survive. If media do not succeed in gratifying unique user needs, in other words, if media lack to create a unique niche for themselves, they will have to compete against other media who fulfil similar functions. As a result of media convergence, smartphone device give access to a large number of mobile applications that vary in functionality, and thus in the needs that they gratify. Using the niche theory lens, we may assume that some of these apps compete against each other, whereas others don’t – for example, when a person uses Google as a search engine, it is unlikely that this same user also actively uses Bing, or another search engine, although these mobile applications may be installed on the phone. Research shows that while people have several mobile applications downloaded and installed on their smartphone, they only use only a handful on a frequent basis (Jung, Kim & Chan-Olmsted, 2014) – this usage concentration in a few applications aligns with the notion of ‘media repertoires’ that users build, presumably to be better able to cope with complex media environments (Heeter, 1985 as cited in Jung, Kim & Chan-Olmst