Weizenbaum Digital Science Center
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Das WDSC ist eine Forschungseinheit, die die Digitalisierungsforschung am Weizenbaum-Institut unterstützt, indem sie die Forschungsgruppen über methodenorientierte Weiterbildung vernetzt, in Kooperation mit den Forschungsgruppen Dateninfrastrukturen bereitstellt und Grundfragen der Digitalisierungsforschung systematisiert und synthetisiert. An dieser Stelle sind die Publikationen, Materialien und Daten zugänglich, die von den vier Forschungseinheiten des WDSC – Weizenbaum Panel, Forschungssynthesen, Metaforschung, Methodenlab – erstellt werden.
English The WDSC is a research unit that supports digitization research at the Weizenbaum Institute by networking the research groups through method-oriented training, providing data infrastructures in cooperation with the research groups, and systematizing and synthesizing fundamental questions in digitization research. The publications, materials and data produced by the four research units of the WDSC—Weizenbaum Panel, Research Syntheses, Meta Research, Methods Lab—are accessible here.
English The WDSC is a research unit that supports digitization research at the Weizenbaum Institute by networking the research groups through method-oriented training, providing data infrastructures in cooperation with the research groups, and systematizing and synthesizing fundamental questions in digitization research. The publications, materials and data produced by the four research units of the WDSC—Weizenbaum Panel, Research Syntheses, Meta Research, Methods Lab—are accessible here.
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Auflistung Weizenbaum Digital Science Center nach Forschungsgruppen "Methodenlab"
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- ItemAlgorithm dependency in platformized news use(2023) Schaetz, Nadja; Gagrčin, Emilija; Toth, Roland; Emmer, MartinPrevious 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.
- ItemBittersweet Symphony: Nostalgia and Melancholia in Music Reception(2023) Toth, Roland; Dienlin, TobiasListening 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.
- ItemMultidimensional Measurement of Mobile Media Use(Weizenbaum Institute, 2021) Toth, RolandJust 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.
- ItemOne App to Assess Them All: Combining surveys, experience sampling, and logging/data donation in an Android and iOS app(2023) Toth, RolandSmartphones 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.
- ItemSharing is Caring - Addressing shared issues and challenges in hate speech research(Digital Communication Research, 2023) Strippel, Christian; Paasch-Colberg, Sünje; Emmer, Martin; Trebbe, JoachimThis book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research.
- ItemSomebody's Watching Me: Smartphone Use Tracking and Reactivity(2021) Toth, Roland; Trifonova, TatianaLike 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.