Systematic Categorisation of 3,091 Smartphone Applications From a Large-Scale Smartphone Sensing Dataset

Schoedel, Ramona; Oldemeier, Michelle; Bonauer, Léonie; Sust, Larissa

doi:10.5334/jopd.59

Cited by 12 publications

(13 citation statements)

References 20 publications

(27 reference statements)

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“…We recorded participants' raw app usage logs containing the names of the app used in each event which, however, were not always informative of the app's functionality. Thus, we subsequently annotated the logged apps with psychologically meaningful labels based on a category system by Schoedel et al (2022). Thereby, we excluded the category system apps because it contained only background apps while our focus was on active, user-initiated app use.…”

Section: Enrichment Strategymentioning

confidence: 99%

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Schoedel¹,

Kunz²,

Bergmann³

et al. 2023

Journal of Personality and Social Psychology

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Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations.

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Section: Enrichment Strategymentioning

confidence: 99%

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Schoedel¹,

Kunz²,

Bergmann³

et al. 2023

Journal of Personality and Social Psychology

Self Cite

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“…From the timestamped call logs, we extracted the number and length of incoming and outgoing calls per ESM episode. In addition, we categorized all apps that were used by more than two participants using the categorization dataset by Schoedel, Oldemeier, et al (2022) together with an updated coding of apps not included therein by two independent raters. This enabled us to categorize 94.48% of all app usage events and 45.43% of all apps ever used in our sample.…”

Section: Mobile Sensing Indicators Of Social Contactmentioning

confidence: 99%

Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing

Krämer¹,

Roos²,

Schoedel³

et al. 2023

Preprint

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Social interactions are crucial to affective well-being. Still, people vary interindividually and intraindividually in their social needs. Social need regulation theories state that mismatches between momentary social desire and actual social contact result in lowered affect, yet empirical knowledge about this dynamic regulation is limited. 306 participants in a sample stratified by age and gender (51% women, M_age = 39.41, range 18-80 years) answered up to 20 questionnaires about social interactions and affect while mobile sensing tracked their conversations, calls, and app usage over two days. Combining preregistered and exploratory analyses, we investigated how momentary affect relates to social dynamics, focusing on two states of mismatch between social desire and social contact: social deprivation (i.e., being alone but desiring social contact) and social oversatiation (i.e., being in contact but desiring to be alone). We used specification curve analyses to scrutinize the operationalization of these constructs. Social oversatiation was associated with decreased positive affect (PA) and increased negative affect (NA). Social deprivation, however, was unrelated to affect. Exploratory multilevel models showed that a higher desire to be alone was consistently associated with decreased affect, whereas a higher desire for social contact was related to increased affect. Mobile sensing data revealed that having more conversations was related to higher PA even when controlling for momentary social desire. Using communication apps more frequently when alone was related to higher NA. We discuss implications for social need regulation, related studies on voluntary solitude, and the advantages of combining experience sampling and mobile sensing assessments.

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“…Finally, the category usage logs includes data types that are generated by the user's interaction with the device. For example, app logs can be summarized into broader categories to extract indicators of certain behaviors (e.g., messenger apps, such as Facebook Messenger, WhatsApp, or Telegram, can be assigned to the category communication, and the time spent in social communication can be extracted; Schoedel, Oldemeier, et al, 2022). These indicators can be used to study daily social behaviors (e.g., Harari, Müller, Stachl, et al, 2020).…”

Section: What Psychological Variables Can Be Sensed?mentioning

confidence: 99%

Mobile Sensing Methods

Mehl¹,

Schoedel

2023

Preprint

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Mobile and wearable devices are equipped with an array of embedded digital sensors that produce continuous steams of digital trace data. These now effectively ubiquitous digital trace data can empower psychologists to study human behavior “in vivo” and “in situ,” which has been an important goal following the American Psychology Association’s “Decade of Behavior” (2000-2009). As a result, there is currently great excitement among psychological scientists about the potentials that sensor-based mobile technologies offer for studying everyday situations and behavior ecologically and at scale. This novel approach to field research is commonly referred to as mobile sensing, or passive sensing. In this chapter we retrace mobile sensing’s journey in social and personality psychology. We begin with briefly reviewing forces that helped pave the way for mobile sensing’s warm welcome in the field. In the second and third part, we provide an overview of the current use of mobile sensing in social and personality psychology and convey a very basic how-to. Finally, we end the chapter with a discussion of where we think mobile sensing currently stands and identify some possible future directions.

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Systematic Categorisation of 3,091 Smartphone Applications From a Large-Scale Smartphone Sensing Dataset

Cited by 12 publications

References 20 publications

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing

Mobile Sensing Methods

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