The Second-Level Smartphone Divide: A Typology of Smartphone Usage Based
on Frequency of Use, Skills, and Types of Activities
- URL: http://arxiv.org/abs/2111.05142v1
- Date: Tue, 9 Nov 2021 13:38:59 GMT
- Title: The Second-Level Smartphone Divide: A Typology of Smartphone Usage Based
on Frequency of Use, Skills, and Types of Activities
- Authors: Alexander Wenz, Florian Keusch
- Abstract summary: This paper examines inequalities in the usage of smartphone technology based on five samples of smartphone owners collected in Germany and Austria between 2016 and 2020.
We identify six distinct types of smartphone users by conducting latent class analyses that classify individuals based on their frequency of smartphone use, self-rated smartphone skills, and activities carried out on their smartphone.
- Score: 77.34726150561087
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper examines inequalities in the usage of smartphone technology based
on five samples of smartphone owners collected in Germany and Austria between
2016 and 2020. We identify six distinct types of smartphone users by conducting
latent class analyses that classify individuals based on their frequency of
smartphone use, self-rated smartphone skills, and activities carried out on
their smartphone. The results show that the smartphone usage types differ
significantly by sociodemographic and smartphone-related characteristics: The
types reflecting more frequent and diverse smartphone use are younger, have
higher levels of educational attainment, and are more likely to use an iPhone.
Overall, the composition of the latent classes and their characteristics are
robust across samples and time.
Related papers
- Learning About Social Context from Smartphone Data: Generalization
Across Countries and Daily Life Moments [5.764112063319108]
We used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from 581 young adults in five countries.
Several sensors are informative of social context, that partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) can achieve similar performance above 90% AUC.
These findings confirm the importance of the diversity of mobile data, to better understand social context inference models in different countries.
arXiv Detail & Related papers (2023-06-01T17:20:56Z) - Understanding the Social Context of Eating with Multimodal Smartphone
Sensing: The Role of Country Diversity [5.764112063319108]
This study focuses on a dataset of approximately 24K self-reports on eating events provided by 678 college students in eight countries.
Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique trends in each country.
arXiv Detail & Related papers (2023-06-01T14:16:59Z) - Complex Daily Activities, Country-Level Diversity, and Smartphone
Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK [6.52702503779308]
Smartphones enable understanding human behavior with activity recognition to support people's daily lives.
People are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings.
We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries.
arXiv Detail & Related papers (2023-02-16T21:34:55Z) - Your Day in Your Pocket: Complex Activity Recognition from Smartphone
Accelerometers [7.335712499936904]
This paper investigates the recognition of complex activities exclusively using smartphone accelerometer data.
We used a large smartphone sensing dataset collected from over 600 users in five countries during the pandemic.
Deep learning-based, binary classification of eight complex activities can be achieved with AUROC scores up to 0.76 with partially personalized models.
arXiv Detail & Related papers (2023-01-17T16:22:30Z) - Self-supervised Hypergraph Representation Learning for Sociological
Analysis [52.514283292498405]
We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria.
First, we propose an effective hypergraph awareness and a fast line graph construction framework.
Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
arXiv Detail & Related papers (2022-12-22T01:20:29Z) - Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits.
Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases.
In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
arXiv Detail & Related papers (2022-03-14T17:05:59Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - Federated and continual learning for classification tasks in a society
of devices [59.45414406974091]
Light Federated and Continual Consensus (LFedCon2) is a new federated and continual architecture that uses light, traditional learners.
Our method allows powerless devices (such as smartphones or robots) to learn in real time, locally, continuously, autonomously and from users.
In order to test our proposal, we have applied it in a heterogeneous community of smartphone users to solve the problem of walking recognition.
arXiv Detail & Related papers (2020-06-12T12:37:03Z) - Mobile social media usage and academic performance [3.893605812705635]
Students are especially sensitive to social media and smartphones because of their pervasiveness.
Several studies have shown that there is a negative correlation between social media and academic performance.
We propose to bridge this gap by parametrizing social media usage and academic performance.
arXiv Detail & Related papers (2020-04-03T06:14:36Z) - Towards Palmprint Verification On Smartphones [62.279124220123286]
Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence.
We built a DCNN-based palmprint verification system named DeepMPV+ for smartphones.
The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments.
arXiv Detail & Related papers (2020-03-30T08:31:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.