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Auflistung Aufsätze nach Forschungsgruppen "Dynamiken der digitalen Mobilisierung"

Gerade angezeigt 1 - 5 von 5
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    “Intervening Is a Good Thing but . . .”: The Role of Social Norms in Users’ Justifications of (Non-)Intervention Against Incivility
    (2023) Gagrčin, Emilija; Milzner, Miriam
    User intervention against incivility as social enforcement of democratic norms on social media platforms is considered an act of “good citizenship” by citizens and scholars alike. However, between ideals and behavior, multiple social norms are at play in shaping individuals’ sense of personal responsibility for intervening. This study explores the role of conflicting norms in situations requiring user intervention against online incivility. By combining the perspectives of norms as expectations and norms as cultural vocabularies, we investigate users’ salient norms, and how these norms influence users’ justifications for (non-)intervention. Based on qualitative interview data from Germany (N = 20), we identified three distinct reasoning patterns employed to justify (non-)intervention: the pragmatic, the dismissive, and the aspirational. By identifying fault lines, our typology points to normative origins of ambivalence related to user intervention. The findings offer insights into strategies to motivate intervention against online incivility.
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    Mapping a Dark Space: Challenges in Sampling and Classifying Non-Institutionalized Actors on Telegram
    (2023) Jost, Pablo; Heft, Annett; Bühling, Kilian; Zehring, Maximilian; Schulze, Heidi; Bitzmann, Hendrik; Domahidi, Emese
    Crafted as an open communication platform characterized by high anonymity and minimal moderation, Telegram has garnered increasing popularity among activists operating within repressive political contexts, as well as among political extremists and conspiracy theorists. While Telegram offers valuable data access to research non-institutionalized activism, scholars studying the latter on Telegram face unique theoretical and methodological challenges in systematically defining, selecting, sampling, and classifying relevant actors and content. This literature review addresses these issues by considering a wide range of recent research. In particular, it discusses the methodological challenges of sampling and classifying heterogeneous groups of (often non-institutionalized) actors. Drawing on social movement research, we first identify challenges specific to the characteristics of non-institutionalized actors and how they become interlaced with Telegram’s platform infrastructure and requirements. We then discuss strategies from previous Telegram research for the identification and sampling of a study population through multistage sampling procedures and the classification of actors. Finally, we derive challenges and potential strategies for future research and discuss ethical challenges.
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    Message Deletion on Telegram: Affected Data Types and Implications for Computational Analysis
    (Taylor & Francis, 2023) Bühling, Kilian
    Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article.
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    Pandemic protesters on Telegram: How platform affordances and information ecosystems shape digital counterpublics
    (2023) Bühling, Kilian; Heft, Annett
    This study analyzes how platform affordances, their appropriation by movement actors, and these actors’ leveraging of information ecosystems—in combination—helped form a digital counterpublic during the COVID-19 pandemic. It draws on public communication data sent by more than 300 Telegram channels and group chats affiliated with the Querdenken movement over a 2-year period, and combines automated and manual text classification with network analysis. The study demonstrates how Telegram afforded connective and collective action in distinct ways that reflected the movement’s organizational structure and aims, as well as the impact of individual information-sharing on the process of movement-building itself. Accounting for time-dependent dynamics, the study also found that different parts of the counterpublic latched onto and sustained distinct information ecosystems to articulate their claims and mobilize contentious action.
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    Veiled conspiracism: Particularities and convergence in the styles and functions of conspiracy-related communication across digital platforms
    (2025) Buehling, Kilian; Zhang, Xixuan; Heft, Annett
    Digital communication venues are essential infrastructures for anti-democratic actors to spread harmful content such as conspiracy theories. Capitalizing on platform affordances, they leverage conspiracy theories to mainstream their political views in broader public discourse. We compared the word choice, language style, and communicative function of conspiracy-related content to understand its platform-dependent differences and convergence. Our cases are the conspiracy theories of the New World Order and Great Replacement, which we analyzed on 4chan/pol/, Twitter, and seven alternative US news media longitudinally from 2011 to 2021. The conspiracy-related texts were comparatively analyzed using a multi-method approach of computational and quantitative text analyses. Our results show that conspiracy narrations are increasingly present in all venues. While language differs vastly between platforms, we observed a style convergence between Twitter and 4chan. The results show how more coded language veils the spread of racist and antisemitic content beyond the so-called dark platforms.