Browsing by Author "Toth, Roland"

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Algorithm dependency in platformized news use
(2023) Schaetz, Nadja; Gagrčin, Emilija; Toth, Roland; Emmer, Martin
Previous research has highlighted the ambiguous experience of algorithmic news curation whereby people are simultaneously comfortable with algorithms, but also concerned about the underlying data collection practices. The present article builds on media dependency theory and news-finds-me (NFM) perceptions to explore this tension. Empirically, we analyze original survey data from six European countries (Germany, Sweden, France, Greece, Poland, and Italy, n = 2,899) to investigate how young Europeans’ privacy concerns and attitudes toward algorithms affect NFM. We find that a more positive attitude toward algorithms and more privacy concerns are related to stronger NFM. The study highlights power asymmetries in platformized news use and suggests that the ambivalent experiences might be a result of algorithm dependency, whereby individuals rely on algorithms in platformized news use to meet their information needs, despite accompanying risks and concerns.
Bittersweet Symphony: Nostalgia and Melancholia in Music Reception
(2023) Toth, Roland; Dienlin, Tobias
Listening to music can cause experiences of nostalgia and melancholia. Although both concepts are theoretically related, to date they have not been analyzed together regarding their emotional and cognitive profiles. In this study, we identify their theoretical underpinnings and determine how they can be measured empirically. We analyze how listening to music causes nostalgia and melancholia, and whether both experiences are related to different behavioral intentions. To this end, we conducted an online experiment with 359 participants who listened to music they considered either nostalgic, melancholic, or neutral. Afterward, participants answered 122 questionnaire items related to nostalgia and melancholia. Using Structural Equation Modeling, and more specifically Multiple Indicators and Multiple Causes Modeling, we first developed two new scales: the Formative Nostalgia Scale and the Formative Melancholia Scale. Both scales consist of five items each. Results showed that listening to music indeed increased nostalgia and melancholia. Although considerably different, the concepts are related. Listening to nostalgic music increases melancholia, whereas listening to melancholic music does not increase nostalgia. Also, both experiences are related to different behavioral intentions. Whereas experiencing nostalgia was associated with a stronger intention to share the music and to listen to it again, experiencing melancholia revealed the exact opposite relation.
Bursting Self-reports? Comparing Sampling Frequency Effects of Mobile Experience Sampling Method on Compliance, Attrition, and Sample Biases
(2025) Ohme, Jakob; Charlton, Timothy; Toth, Roland; Araujo, Theo; De Vreese, Claes H.
In-situ measurements, using the experience sampling method (ESM), can provide insight into behaviors and contextual factors by allowing individuals to self-report them via text or push messages on a smartphone close to the behavior of interest. However, more is needed to know about the data quality of these measures, particularly the impact of sampling frequency. This study aims to examine the effects of different sampling frequencies on compliance, sample biases, and reactivity of measures in the context of digital media use. In July 2021, a group of Dutch citizens (n=250) was randomly assigned to either a standard daily-intensive burst measure (DI-BM; seven surveys across the day) or hourly-intensive burst measure (HI-BM; 12 surveys over two hours per day) condition and surveyed across seven consecutive days, resulting in a total number of 16,135 surveys sent. Results indicate higher compliance in the standard ESM condition than in the burst ESM condition.
Can Intergroup Contact in Virtual Reality (VR) Reduce Stigmatization Against People with Schizophrenia?
(2021) Stelzmann, Daniela; Toth, Roland; Schieferdecker, David
People with mental disorders such as schizophrenia do not only suffer from the symptoms of their disorders but also from the stigma attached to it. Although direct intergroup contact is an effective tool to reduce stigmatization, it is rare in real life and costly to be established in interventions, and the success of traditional media campaigns is debatable. We propose Virtual Reality (VR) as a low-threshold alternative for establishing contact since it involves less barriers for affected and unaffected persons. In a 2 + 1 experiment (n = 114), we compared the effects of encounters with a person with schizophrenia through a VR video with contact through a regular video and no contact at all on anxiety, empathy, social proximity, and benevolence towards people with schizophrenia. We found that contact via VR reduced stigmatization only for participants who liked the person encountered. Our data suggest that it is crucial how participants evaluate the person that they encounter and that stronger perception of spatial presence during reception plays an important role, too. Therefore, we discussvarious boundary conditions that need to be considered in VR interventions and future research on destigmatization towards mental disorders, especially schizophrenia.
Extracting Meaningful Measures of Smartphone Usage from Android Event Log Data: A Methodological Primer
(2025) Parry, Douglas; Toth, Roland
As smartphones become increasingly integral to daily life, their importance for understanding human behavior will only continue to grow. Recognizing the potential of objective data on smartphone usage and the challenges associated with raw Android event log data, this paper provides a foundational guide for extracting meaningful measures of smartphone usage from such data. We describe the characteristics of Android event log data, define key smartphone usage types (i.e., glances, sessions, and episodes), and briefly discuss common challenges in handling these data. The core of the paper presents a detailed practical procedure to extract relevant usage metrics (sessions, glances, app episodes) from raw Android event logs, described visually, verbally, and with pseudo-code (with sample data and code in R available in the supplementary materials). This guide aims to equip researchers with the knowledge and tools to effectively utilize Android event log data, advancing knowledge of smartphone use patterns and their effects.
From Screen Time to Daily Rhythms: A Mixed Methods Study of Smartphone Use Among German Adults
(2025) Toth, Roland; Parry, Douglas; Emmer, Martin
Understanding typical smartphone behavior increasingly relies on device-enabled fine-grained data sources that go beyond retrospective self-reports. This study contributes to this knowledge by studying how, when, and under what conditions people engage with their smartphones, using a rich mixed-method dataset that combines Android logging, iOS data donation, and mobile experience sampling. The dataset captures both the quantity and quality of smartphone use among a large, quota-targeted sample of German adults (n = 1,797). The descriptive findings indicate that smartphone engagement is characterized by frequent interactions. Most sessions last under seven minutes, and app use rarely exceeds two minutes. Usage rhythms vary throughout the day: shorter glances dominate during constrained periods, while longer sessions cluster around mornings and evenings. Younger users display more fragmented usage patterns, whereas older adults tend to engage in fewer but longer sessions. These patterns reflect the situational affordances and gratifications of different types of mobile interactions and highlight the temporal structure of smartphone use. By mapping these rhythms and use types, our findings offer a foundation for theorizing about the cognitive, behavioral, and emotional consequences of smartphone use and provide practical guidance for researchers employing intensive longitudinal and real-time measurement approaches.
Multidimensional Measurement of Mobile Media Use
(Weizenbaum Institute, 2021) Toth, Roland
Just like all types of media use, mobile media use is usually measured using retrospective, self-reported indications of quantity in the form of duration and frequency. This is not only problematic due to the fact that people misjudge their own use to a great extent, but also because theoretical approaches predominantly suggest that mere contact is not sufficient for the description of media use. This especially holds for mobile media use, as specific contact episodes are not easily distinguishable anymore due to their short duration and high frequency. Mobile media use is rather characterized by circumstances surrounding the contact itself - they are used for countless purposes, in a habitual manner, and in various situations. In this paper, I am proposing a renewed, multidimensional measure of mobile media use that takes into account these characteristics in addition to well-known measures of quantity and suggest methods for assessing its convergent and content validity.
One App to Assess Them All: Combining surveys, experience sampling, and logging/data donation in an Android and iOS app
(2023) Toth, Roland
Smartphones have become popular tools for data collection in the social sciences due to their high prevalence and mobility. Surveys, experience sampling (ESM) and tracking/logging are among the most used smartphone data-collection methods. However, existing apps are either commercial solutions, require programming skills, collect sensitive data, or do not handle all three methods simultaneously. When two or more data collection methods are used simultaneously, it further burdens both researchers and participants. This paper introduces the app MART (Mobile Assessment Research Tool) that solves these problems and is available for Android and iOS devices. Content and data collection settings can be customized dynamically via a web interface without the need to compile a new version of the app when changes are made. While the logging functionality is only supported on Android devices, data donation via the app Screen Time is requested on iOS devices. MART is already functional, and the source code is open-source and available on GitHub. The necessary long-term revisions for its use in custom projects without reprogramming are currently under development.
Quality and Quantity: The Role of Gratification and Situation Variety when Measuring Mobile Media Use
(2025-11-01) Toth, Roland; Fernández, Aurelio; García-Manglano, Javier; de la Rosa, Pedro
Mobile media use is usually measured through its quantity, particularly duration, in research. We argue that this approach overlooks how people use these versatile and complex devices. In this article, we explore the role of the variety of gratifications and situations as indicators of mobile media use quality. To investigate whether these dimensions can contribute to the measurement of mobile media use, we validate them against mobile vigilance. Using three waves of data (3,194 questionnaires) from a representative sample of 1,525 Spanish emerging adults (aged 19–25), we estimated a Multilevel Structural Equation Model. Our findings reveal that the variety of gratifications and situations are more strongly related to mobile vigilance an indicator of the relevance of mobile media in daily life than duration is. We advocate for considering both quantity and quality when measuring mobile media use to gain a deeper understanding of its dynamics and close relationship with mobile vigilance.
Smartphone Use in Germany in 2023: A Mixed-Method Investigation
(SocArXiv, 2024) Toth, Roland; Parry, Douglas A.; Emmer, Martin
Our understanding of typical smartphone behavior has only recently begun to advance due to the accessibility of increasingly valid data sources. Beyond analysing the frequency, duration, and content of smartphone activities, there is substantial value in understanding when people engage in particular forms of mobile media use, and data collection methods that go beyond simple retrospective self-reports provide the means to do so. This paper contributes to our understanding of contemporary smartphone usage patterns and their temporal dynamics by employing a mixed-method dataset obtained through Android logging, iOS data donation, and mobile experience sampling methods. This dataset captures both the quantity and quality of smartphone use among a large, quota-targeted sample of German adults (n = 2032). The findings provide a comprehensive view of smartphone use, examining both overall trends and daily rhythms. They reveal that smartphone usage is typified by frequent, short interactions, with younger users displaying more fragmented patterns compared to older counterparts, alongside variations in the gratifications derived across age groups. These findings lay the groundwork for further theorization about the causes, nature, and consequences of the observed usage patterns, while also offering essential contextual and methodological insights for researchers employing intensive longitudinal approaches to evaluate smartphone usage.
Somebody's Watching Me: Smartphone Use Tracking and Reactivity
(2021) Toth, Roland; Trifonova, Tatiana
Like all media use, smartphone use is mostly being measured retrospectively with self-reports. This leads to misjudgments due to subjective aggregations and interpretations that are necessary for providing answers. Tracking is regarded as the most advanced, unbiased, and precise method for observing smartphone use and therefore employed as an alternative. However, it remains unclear whether people possibly alter their behavior because they know that they are being observed, which is called reactivity. In this study, we investigate first, whether smartphone and app use duration and frequency are affected by tracking; second, whether effects vary between app types; and third, how long effects persist. We developed an Android tracking app and conducted an anonymous quasi-experiment with smartphone use data from 25 people over a time span of two weeks. The app gathered not only data that were produced after, but also prior to its installation by accessing an internal log file on the device. The results showed that there was a decline in the average duration of app use sessions within the first seven days of tracking. Instant messaging and social media app use duration show similar patterns. We found no changes in the average frequency of smartphone and app use sessions per day. Overall, reactivity effects due to smartphone use tracking are rather weak, which speaks for the method's validity. We advise future researchers to employ a larger sample and control for external influencing factors so reactivity effects can be identified more reliably.
Supplementary Material for “Somebody’s Watching Me: Smartphone Use Tracking and Reactivity”
(2021) Toth, Roland; Trifonova, Tatiana
## Files + CHBR/analysis.Rmd + CHBR/data_all.csv + CHBR/visualization_all.pdf
Umfrage zu Forschungsdatenmanagement am Weizenbaum-Institut
(Zenodo, 2021) Toth, Roland; Vuorimäki, Julian; Schimmler, Sonja; Krzywdzinski, Martin; Friesike, Sascha; Neuberger, Christoph; Oellers, Claudia
This dataset contains responses to a survey on open data and open access amongst members of the Weizenbaum Institute for the Networked Society which ran from 30 August to 21 September 2021. The survey elicited 39 valid responses out of 181 potential respondents working at the institute. Contributors (according to CRediT): Roland Toth Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Validation. Julian Vuorimäki Roles: Conceptualization, Investigation, Project Administration, Validation. Sonja Schimmler Roles: Conceptualization, Supervision. Martin Krzywdzinski Roles: Conceptualization, Supervision. Sascha Friesike Roles: Conceptualization, Supervision. Christoph Neuberger Roles: Conceptualization, Supervision. Claudia Oellers Roles: Conceptualization, Project Administration, Supervision.
We and AI - Living in a Datafied World: Experiences & Attitudes of Young Europeans
(Weizenbaum Institute, 2021) Gagrčin, Emilija; Schaetz, Nadja; Rakowski, Niklas; Toth, Roland; Renz, André; Vladova, Gergana; Emmer, Martin
Zooming in on Smartphone Habits: Identifying Behavioral Indicators of Perceived Automaticity
(2025) Toth, Roland; Parry, Douglas A.; Pourafshari, Razieh; Bayer, Joseph
Research suggests that a large portion of media use is driven by habit. Yet our ability to measure habitual processes directly remains limited, suggesting that more naturalistic methods are needed. Leveraging a deep mobile event log and experience sampling dataset of German Internet users (N = 889), we probe the situational dynamics of smartphone habits. We investigate how moment-to-moment smartphone behavior corresponds to the perception of automaticity, with implications for the measurement of habitual behavior more generally. Contrary to expectations, duration – rather than frequency – of smartphone behavior emerged as the more consistent predictor of situational habit perception at both within- and between-person levels. However, these links varied by the type of behavior, with sessions and episodes (but not glances) relating to perceived automaticity. Additionally, home screen and gateway app use were not associated with perceived automaticity. Our results generate new insights – and deep questions – into the nature of real-world media habits.