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    The platform matters: cross-platform differences in data donation willingness, behavior, and bias
    (Taylor & Francis, 2025-12) Wedel, Lion; Ohme, Jakob; Mayer, Anna-Theresa; Gaisbauer, Felix; Fan, Yangliu
    Data donations are a method to access user-level digital trace data, as they provide fine-grained measures of content exposure on social media. The interest in data donation as a data collection method is accompanied by a broad uncertainty about the reasons that drive the donation of data by users. The current literature lacks comparative analysis across various platforms. This study investigates platform-specific predictors for data donation behavior of a non-probability quota sample of German social media users (N = 2,296) for YouTube, Facebook, Instagram, and TikTok and the resulting non-response biases. Based on the analysis of 340 data donation packages, we find that participants are less likely to donate TikTok data compared to the other platforms. Gender is the main driver during the willingness step for drop offs, while political leaning is a key predictor for all platforms except Facebook during the donation stage. Data donors tend to self-report less active social media usage with news and political content than those who do not donate data. Our findings highlight the importance of considering platform-specific differences in expected donation rates, biases, and the potential for discrepancies between indicated willingness and actual donation behavior when designing and interpreting data donation studies.
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    Gender-specific homophily on Instagram and implications on information spread
    (2024) Pignolet, Yvonne-Anne; Schmid, Stefan; Seelisch, Arne
    More and more social interactions happen online. On online social networks such as Instagram, millions of users share, like, and comment on photos and videos every day, interacting with other users world wide, at large scale and at a high rate. These networks do not only introduce new user experiences, but they also enable new insights into human behavior. Here, we use these new possibilities to study homophilic behavior—the tendency of individuals to bond with people similar to themselves. While homophilic behavior has been observed in many contexts, little is known about gender-specific differences and the extent of homophilic behavior of female and male users in online social networks. Based on a unique and extensive data set, covering over 800,000 (directed) Instagram interactions and a time span of three years, we shed light on differences between genders and uncover an intriguing asymmetry of homophily. In particular, we show that female users exhibit homophily to a larger extent than male users. The magnitude of this asymmetry depends on the type of interaction, as differences are more pronounced for ‘comment’-interactions than for ‘like’-interactions. Given these empirical observations, we further study the implications of such gender differences on the spread of information in social networks in a basic model. We find that on average, a piece of information that originates from a female group reaches significantly more female users than male users.
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    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.
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    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.
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    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.
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    Von der Theorie zur Praxis: Weniger Fehler und schnellere Umsetzung von Produktionsprozessen dank Augmented Reality
    (2025) Gonnermann-Müller, Jana; Wotschack, Philip; Krzywdzinski, Martin; Gronau, Norbert
    Die zunehmende Komplexität industrieller Umgebungen erfordert neue Kompetenzen, insbesondere in der Interaktion mit digitalen Systemen. Traditionelle Ausbildungsmethoden reichen für den effektiven Transfer von angewandtem Wissen oft nicht aus. Um diese Lücke zu schließen, wurde ein Experiment durchgeführt, bei dem Augmented Reality (AR) und papierbasierte Anleitungen in einem Produktionsszenario verglichen wurden. Die Ergebnisse zeigen: Teilnehmer, die mit AR lernten, führten den Produktionsprozess deutlich schneller und mit weniger Fehlern durch. Darüber hinaus berichteten die Lernenden, die AR nutzten, von einer höheren Benutzerfreundlichkeit und einer geringeren kognitiven Belastung während des Trainings.
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    Unravelling passive social media use through screenomes
    (2024) Yee, A.Z.H; Krause, Hannes-Vincent; Meier, Adrian; Ng, Li Yin; Loy, Guang Peng
    The active-passive framework to social media use and well-being promised nuanced insights, yet effects of passive use have been mixed. One reason could be the enormous heterogeneity of the ‘passive use’ concept, which encompasses various social media features, actions, and contents. This study applies the conceptual lenses of the hierarchical CMC taxonomy and social media content genres to unpack and explore this heterogeneity of passive social media use. We use a random sample of smartphone screenshots (‘screenomics’) drawn from 703,827 sequential screenshots collected from 20 participants over a two-week period to content analyze 10,000 screenshots. Results reveal substantial heterogeneity and patterns with in passive use across four analytical levels: branded application, feature, modality, and content topics/genres. As the effects of social media on well-being may often be caused by content or design features, our study provides insights into how passive use can be broken down into more meaningful units of analysis.
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    Perceptions, hopes, and concerns regarding the possibilities of artificial intelligence in weather warning contexts
    (2025) Kox, Thomas; Harrison, Sara; Ziegler, Ferdinand; Gerhold, Lars
    Artificial intelligence (AI) is increasingly used in disaster risk reduction, including early warning systems (EWS) for weather hazards. While AI promises faster data processing and improved forecast accuracy, concerns remain about automation bias, reduced human oversight, or accountability, and erosion of professional judgment. Despite rapid technological advances, the perceptions of the weather warning community remain underrepresented in current research. To address this, we conducted an Argumentative Delphi study with experts from the 2024 WMO HIWeather Final Conference. Participants assessed AI's impact on 13 key aspects of weather warnings – including quality, interpretability, accountability, and social bias – and shared hopes and concerns. Overall, participants expressed cautious optimism. AI is expected to improve the goodness of warnings, potentially cascading into broader dimensions of warning efficacy, public trust, and institutional responsibility. However, concerns include over-reliance on AI, erosion of human involvement, and challenges in maintaining a single authoritative voice in warning communication. Rather than viewing AI as replacement for human decision-making, it is seen as decision-support tool that augments professional expertise. Tailored warnings and multilingual communication emerged as promising areas for AI application, though issues of data bias and accessibility remain. Thus, ethical implementation is vital to ensure inclusiveness and alignment global disaster risk reduction goals. Finally, the introduction of AI touches the ‘professional core’ of weather warning as an occupation and prompts experts to define their evolving roles and core competencies in the face of technological advancements. Future research should explore how generative AI may reshape forecasting and the profession itself.
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    Opportunities for extremism: a comparative study of German far-right social movement networks on Twitter/X, Telegram, and Gettr
    (2025) Gong, Baoning
    This paper contributes to platform-comparative research through a case study of Twitter/X, Telegram, and Gettr in the context of German far-right social movements. It introduces the conceptual framework of platform opportunity structures to examine how platforms enable or constrain far-right mobilization. Using community analysis of sharing networks among the same pool of far-right social movement actors, the study explores how technological affordances, governance models, ownership and branding, and user bases and cultures shape platform-specific networking patterns. The findings reveal Telegram as a central platform for the most radical and active communities; Twitter as a site where anti-elite journalists and politicians are salient; and Gettr as a platform to connect to the U.S. far right. While anti-elite and COVID-related conspiracist figures exert influence on all platforms-particularly on Telegram, the prevalence of AfD politicians, pseudonymous amplifiers, and transnational ties to the U.S. far right – especially after Elon Musk’s acquisition of Twitter – are emergent factors. As Twitter shifts toward ‘alternative’ platforms like Telegram and Gettr, offering minimal moderation and security and even ideological branding, this article adds to understanding how dynamic platform opportunity structures shape far-right mobilization online.
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    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.