Weizenbaum Institute

Over the last decade, digital sovereignty has become a central element in policy discourses on digital issues. Although it has become popular in both centralised/authoritarian and democratic countries alike, the concept remains highly contested. After investigating the challenges to sovereignty apparently posed by the digital transformation, this essay retraces how sovereignty has re-emerged as a key category with regard to the digital. By systematising the various normative claims to digital sovereignty, it then goes on to show how, today, the concept is understood more as a discursive practice in politics and policy than as a legal or organisational concept.

The term Metaverse is emerging as a result of the late push by multinational technology conglomerates and a recent surge of interest in Web 3.0, Blockchain, NFT, and Cryptocurrencies. From a scientific point of view, there is no definite consensus on what the Metaverse will be like. This paper collects, analyzes, and synthesizes scientific definitions and the accompanying major characteristics of the Metaverse using the methodology of a Systematic Literature Review (SLR). Two revised definitions for the Metaverse are presented, both condensing the key attributes, where the first one is rather simplistic holistic describing “a three-dimensional online environment in which users represented by avatars interact with each other in virtual spaces decoupled from the real physical world”. In contrast, the second definition is specified in a more detailed manner in the paper and further discussed. These comprehensive definitions offer specialized and general scholars an application within and beyond the scientific context of the system science, information system science, computer science, and business informatics, by also introducing open research challenges. Furthermore, an outlook on the social, economic, and technical implications is given, and the preconditions that are necessary for a successful implementation are discussed.

Right‐wing online news media have emerged in many countries as an important force in the media landscape, positioning themselves as an alternative to a perceived political and media mainstream. This article studies these sites as a cornerstone of right‐wing digital news infrastructures in six Western democracies (Sweden, Denmark, Germany, Austria, the United Kingdom, and the United States). Drawing on content analyses of websites and social media accounts on Facebook and Twitter as well as on audience metrics, the article analyses content supply and audience demand structures, as well as organizational and thematic characteristics of seventy alternative right‐wing online news sites. We find that a country’s media and political context, in particular the representation of right‐wing positions in the political and legacy media sphere, can explain variation in the supply of—and demand for—right‐wing news across countries, but is mitigated by transnational audiences. At the same time, we can account for cross‐national heterogeneity of news sites, ranging from sites with a “normalized” appearance to more radical sites that clearly set themselves apart from legacy news outlets in terms of their thematic categories, their funding strategy, and their organizational transparency, leading to various types of digital right‐wing “alternatives” to mainstream news.

Abstract The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with data-based teaching and learning solutions, and they are permanently transforming the market. However, despite the current market dynamics, there are hardly any business models that implement the possibilities of Learning Analytics (LA) and Artificial Intelligence (AI) to create adaptive teaching and learning paths. This paper focuses on EdTech companies and the drivers and barriers that currently affect data-based teaching and learning paths. The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning Analytics and adaptive teaching and learning (AI based). The discourse analysis reveals a diametrical relationship between the traditional educational ideal and the futuristic idea of education and knowledge transfer. While the desire for flexibility and individualization drives the debate on AI-based learning systems, a lack of data sovereignty, uncertainty and a lack of understanding of data are holding back the development and implementation of appropriate solutions at the same time.

This article explains variation in levels of party system institutionalization in Asia by testing available data against several major hypotheses in the literature. The authors make three contributions to the literature on party system institutionalization. First, this study finds that historical legacies are a crucial variable affecting current levels of party system institutionalization. In particular, the immediate postwar period was the crucible from which institutionalized party systems in Asia developed. Second, the authors claim that for a significant number of institutionalized party systems, historical legacies are rooted in some element of authoritarianism, either as former authoritarian parties or as semidemocratic regimes. Third, precisely because authoritarianism has played an important role in the origins of institutionalized party systems, the authors argue that the concept of institutionalization needs to be strictly separated from the concept of democracy.

Political scientists have long attempted to measure and describe the modest and contingent effects of party on the behavior of members of Congress. Recent efforts have extended the debate to the more specific question of whether or not party influences are sufficiently strong to move policy outcomes away from the median position. In this article, we specify four theories of legislative behavior. One is a preference‐based, or partyless, theory of behavior. This theory posits that there are no party effects independent of preferences and that equilibrium outcomes are located at the chamber's median. The other theories rely on different conceptions of the foundations of party effects and yield distinctive predictions about the legislators who will support bills on final passage votes. After testing, our conclusion is that strong party influences can be found in final passage voting in the House: the partyless theory receives little support, but a model based on majority party agenda control works well. Legislative outcomes are routinely on the majority party's side of the chamber median.

In social media effects research, the role of specific social media content is understudied, in part attributable to the fact that communication science previously lacked methods to access social media content directly. Digital trace data (DTD) can shed light on textual and audio-visual content of social media use and enable the analysis of content usage on a granular individual level that has been previously unavailable. However, because digital trace data are not specifically designed for research purposes, collection and analysis present several uncertainties. This article is a collaborative effort by scholars to provide an overview of how three methods of digital trace data collection - APIs, data donations, and tracking - can be used in studying the effects of social media content in three important topic areas of communication research: misinformation, algorithmic bias, and well-being. We address the question of how to collect raw social media content data and arrive at meaningful measures with multiple state-of-the-art data collection techniques that can be used to study the effects of social media use on different levels of detail. We conclude with a discussion of best practices for the implementation of each technique, and a comparison of their advantages and disadvantages.

Previous research offers equivocal results regarding the effect of social networking site use on individuals’ self-esteem. We conduct a systematic literature review to examine the existing literature and develop a theoretical framework in order to classify the results. The framework proposes that self-esteem is affected by three distinct processes that incorporate self-evaluative information: social comparison processes, social feedback processing, and self-reflective processes. Due to particularities of the social networking site environment, the accessibility and quality of selfevaluative information is altered, which leads to online-specific effects on users’ self-esteem. Results of the reviewed studies suggest that when a social networking site is used to compare oneself with others, it mostly results in decreases in users’ selfesteem. On the other hand, receiving positive social feedback from others or using these platforms to reflect on one’s own self is mainly associated with benefits for users’ self-esteem. Nevertheless, inter-individual differences and the specific activities performed by users on these platforms should be considered when predicting individual effects.

The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels. Previous human-centered investigations have largely focused on annotators? subjectivity as a major cause of biased labels. We propose a wider view on this issue: guided by constructivist grounded theory, we conducted several weeks of fieldwork at two annotation companies. We analyzed which structures, power relations, and naturalized impositions shape the interpretation of data. Our results show that the work of annotators is profoundly informed by the interests, values, and priorities of other actors above their station. Arbitrary classifications are vertically imposed on annotators, and through them, on data. This imposition is largely naturalized. Assigning meaning to data is often presented as a technical matter. This paper shows it is, in fact, an exercise of power with multiple implications for individuals and society.

In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring-summer 2020 semester. Our study focused on (1) the students' acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study's results and derive short- and long-term implications for science and practice.

In the battle against misinformation, do negative spillover effects of communicative efforts intended to protect audiences from inaccurate information exist? Given the relatively limited prevalence of misinformation in people’s news diets, this study explores if the heightened salience of misinformation as a persistent societal threat can have an unintended spillover effect by decreasing the credibility of factually accurate news. Using an experimental design (N = 1305), we test whether credibility ratings of factually accurate news are subject to exposure to misinformation, corrective information, misinformation warnings, and news media literacy (NML) interventions relativizing the misinformation threat. Findings suggest that efforts like warning about the threat of misinformation can prime general distrust in authentic news, hinting toward a deception bias in the context of fear of misinformation being salient. Next, the successfulness of NML interventions is not straight forward if it comes to avoiding that the salience of misinformation distorts people’s creditability accuracy. We conclude that the threats of the misinformation order may not just be remedied by fighting false information, but also by reestablishing trust in legitimate news.

Abstract Technologies to measure gaze direction and pupil reactivity have become efficient, cheap, and compact and are finding increasing use in many fields, including gaming, marketing, driver safety, military, and healthcare. Besides offering numerous useful applications, the rapidly expanding technology raises serious privacy concerns. Through the lens of advanced data analytics, gaze patterns can reveal much more information than a user wishes and expects to give away. Drawing from a broad range of scientific disciplines, this paper provides a structured overview of personal data that can be inferred from recorded eye activities. Our analysis of the literature shows that eye tracking data may implicitly contain information about a user’s biometric identity, gender, age, ethnicity, body weight, personality traits, drug consumption habits, emotional state, skills and abilities, fears, interests, and sexual preferences. Certain eye tracking measures may even reveal specific cognitive processes and can be used to diagnose various physical and mental health conditions. By portraying the richness and sensitivity of gaze data, this paper provides an important basis for consumer education, privacy impact assessments, and further research into the societal implications of eye tracking.

This article explicates the mechanisms through which presidential elections shape the legislative party system, an issue that has received little attention to date. The authors argue that presidential elections exert their influence through two distinct channels. First, they affect the incentives of candidates, voters, and parties to coordinate within electoral districts. Second and most importantly, they shape the incentives of candidates to coordinate across legislative electoral districts under a common party banner, leading to more aggregated or nationalized party systems when there are few presidential candidates. The authors find support for the relative importance of this cross-district effect using a unique data set of district-level election results from approximately 600 elections in 70 countries.

Zusammenfassung Seit der Ausbreitung des SARS-CoV-2-Virus in Europa Anfang 2020 wir dan technischen Lösungen zur Eindämmung der Pandemie gearbeitet. Unter den verschiedenen Systementwürfen stechen jene hervor, die damit werben, datenschutzfreundlich und DSGVO-konform zu sein. Die DSGVO selbst verpflichtet die Betreiberïnnen umfangreicher Datenverarbeitungssysteme wie etwa Tracing-Apps zur Anfertigung einer Datenschutz-Folgenabschätzung (DSFA) aufgrund des hohen Risikos für die Rechte- und Freiheiten (Art. 35 DSGVO). Hierbei handelt es sich um eine strukturierte Risikoanalyse, die mögliche grundrechtsrelevante Folgen einer Datenverarbeitung im Vorfeld identifiziert und bewertet. Wir zeigen in unserer DSFA, dass auch die aktuelle, dezentrale Implementierung der Corona-Warn-App zahlreiche gravierende Schwachstellen und Risiken birgt. Auf der rechtlichen Seite haben wir die Legitimationsgrundlage einer freiwilligen Einwilligung untersucht und formulieren die begründete Forderung, dass der Einsatz einer Tracing-App gesetzlich geregelt werden muss. Weiterhin wurden Maßnahmen zur Verwirklichung von Betroffenenrechten nicht ausreichend betrachtet. Nicht zuletzt ist die Behauptung, ein Datum sei anonym, hoch voraussetzungsreich. Anonymisierung muss als ein kontinuierlicher Vorgang begriffen werden, der eine Abtrennung des Personenbezugs zum Ziel hat und auf dem Zusammenspiel von rechtlichen, organisatorischen und technischen Maßnahmen beruht. Der derzeit vorliegenden Corona-Warn-App fehlt es an einem solchen expliziten Trennungsvorgang. Unsere DSFA zeigt dabei auch die wesentlichen Defizite der offiziellen DSFA der Corona-Warn-App auf.

Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platforms. This paper investigates outsourced ML data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor. Our dispositif analysis comprises the examination of 210 data work instruction documents, 55 interviews with data workers, managers, and requesters, and participant observation. Our findings show that discourses encoded in instructions reproduce and normalize the worldviews of requesters. Precarious working conditions and economic dependency alienate workers, making them obedient to instructions. Furthermore, discourses and social contexts materialize in artifacts, such as interfaces and performance metrics, limiting workers' agency and normalizing specific ways of interpreting data. We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.

Abstract Besides various other privacy concerns with mobile devices, many people suspect their smartphones to be secretly eavesdropping on them. In particular, a large number of reports has emerged in recent years claiming that private conversations conducted in the presence of smartphones seemingly resulted in targeted online advertisements. These rumors have not only attracted media attention, but also the attention of regulatory authorities. With regard to explaining the phenomenon, opinions are divided both in public debate and in research. While one side dismisses the eavesdropping suspicions as unrealistic or even paranoid, many others are fully convinced of the allegations or at least consider them plausible. To help structure the ongoing controversy and dispel misconceptions that may have arisen, this paper provides a holistic overview of the issue, reviewing and analyzing existing arguments and explanatory approaches from both sides. Based on previous research and our own analysis, we challenge the widespread assumption that the spying fears have already been disproved. While confirming a lack of empirical evidence, we cannot rule out the possibility of sophisticated large-scale eavesdropping attacks being successful and remaining undetected. Taking into account existing access control mechanisms, detection methods, and other technical aspects, we point out remaining vulnerabilities and research gaps.

The advent of online technologies has been triggering a wave of empirical examinations of online political participation (OPP) over the past twenty years. It also stimulated scholarly debate on how to conceptualize political participation in a digital age. Scholars differ on whether to consider passive and expressive online behaviors part of or a mere precursor to political participation. This study argues that due to its rapid evolution as well as its dependence on platform affordances, quantitative empirical studies on OPP may be prone to deviations between established, much-cited definitions and measurements applied in the field. Based on a systematic literature review of 289 international peer-reviewed survey-based and experimental studies, we analyze both definitions and measurements of OPP. We find a series of disconnections: Measures preponderantly address online activities, yet merely a small share of definitions focuses on the online sphere. While only few definitions account for passive activities (e.g., reading news about politics), many operationalizations include measures capturing such passive behaviors. Expressive activities are most popular in measures of OPP, but definitions rarely reflect this focus. Finally, while measures of OPP are prone to be platform-specific, definitions tend to neglect this characteristic. We conclude by reflecting the conceptual implications of common measurement practices for the study of OPP.

Abstract Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation within organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.

Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy automation”. We drew a sample of 122,884 unique user Twitter accounts that had produced 263,821 tweets contributing to five political discourses in five Western democracies. While all three bot detection methods classified accounts as bots in all our cases, the comparison shows that the three approaches produce very different results. We discuss why neither manual validation nor triangulation resolves the basic problems, and conclude that social scientists studying the influence of social bots on (political) communication and discourse dynamics should be careful with easy-to-use methods, and consider interdisciplinary research.

Current research on visual media and the far right creates two expectations: that memes play an increasingly salient role in the far right’s digital visual culture, and that the visual and participatory dimensions of internet culture facilitate greater transnationality. We explore these expectations with a comparative research design, situating memes in relation to other genres of visual content and across different country contexts. Taking a mixed methods approach, this article examines the digital visual culture of 25 far-right alternative media and other non-party organisations in Australia, Italy, Germany, and the United States. We assess the salience of memes and other visual genres, as well as three forms of transnationality: the circulation of images, direct communicative references, and transnational similarities. Unexpectedly, we find that memes play only a limited role in the digital visual culture of far-right non-party organisations, with their uneven concentration in Anglophone alt-media suggesting the potential pitfalls of assumptions about ‘global’ internet culture. We also find little evidence of transnationality through the circulation of the same visuals across countries, whether memes or other genres. Instead, transnationality works through transnational references within the images themselves and through more parallel practices of reproducing visuals in similar ways with similar themes, but with elements specific to an organisation’s national and political context. Within this, we identify three distinct visual discourses – fascist continuity, western civilisational identity, and pop cultural appropriation – which highlight different practices of transnationality and collective identity construction within the far right online.