Digitale Märkte und Öffentlichkeiten auf Plattformen

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    Chat groups as local civic infrastructure: A case study of “Solidary neighborhood help” Telegram groups during the COVID-19 pandemic in Germany
    (2025) Pasitselska, Olga; Buehling, Kilian; Gagrčin, Emilija
    Messaging groups are emerging as “meso-spaces”—digital environments that enable sustained dialogue and collective action through their distinct affordances. We examine how such spaces facilitate civic self-organization through their hybrid online/offline, public/private, and local/global dynamics and how they function as local civic infrastructure during times of crisis. Using a mixed-methods analytical approach, we examined 47 public Telegram groups from Germany during the COVID-19 pandemic. We identified a fundamental tension between political discussion and practical help in these spaces, resolvable through active horizontal participation (including norm negotiation and self-moderation), or strict vertical moderation. Additional challenges included a lack of access to vulnerable groups and limited outreach to local civil society actors, both of which hindered group activity and structural connections within local civic infrastructure. Despite these challenges, our study highlights the potential of local chat groups for self-organization, albeit primarily among privileged urban individuals. We discuss the implications for democratic theory and practice.
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    Attributing Coordinated Social Media Manipulation: A Theoretical Model and Typology.
    (2025) Thiele, Daniel; Milzner, Miriam; Gong, Baoning; Pfetsch, Barbara; Heft, Annett
    Social media are key arenas for public opinion formation, but are susceptible to coordinated social media manipulation (CSMM), that is, the orchestrated activity of multiple accounts to increase content visibility and deceive audiences. Despite advances in detecting and characterizing CSMM, the attribution problem—identifying the principals behind CSMM campaigns—has received little scholarly attention. In this article, we address this gap by synthesizing existing research and developing a theoretical model for understanding CSMM. We propose a consolidated definition of CSMM, identify its key observable and hidden characteristics, and present a rational choice model for inferring principals’ strategic decisions from campaign features. In addition, we present a typology of CSMM campaigns, linking variations in scale, elaborateness, and disguise to principals’ resources, stakes, and influence strategies. Our contribution provides researchers with conceptual and heuristic tools for attribution and invites interdisciplinary and comparative research on CSMM campaigns.
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    Exploring temporal dynamics in digital trace data: mining user-sequences for communication research
    (arXiv, 2025) Fan, Yangliu; Ohme, Jakob; Wedel, Lion
    Communication is commonly considered a process that is dynamically situated in a temporal context. However, there remains a disconnection between such theoretical dynamicality and the non-dynamical character of communication scholars' preferred methodologies. In this paper, we argue for a new research framework that uses computational approaches to leverage the fine-grained timestamps recorded in digital trace data. In particular, we propose to maintain the hyper-longitudinal information in the trace data and analyze time-evolving 'user-sequences,' which provide rich information about user activity with high temporal resolution. To illustrate our proposed framework, we present a case study that applied six approaches (e.g., sequence analysis, process mining, and language-based models) to real-world user-sequences containing 1,262,775 timestamped traces from 309 unique users, gathered via data donations. Overall, our study suggests a conceptual reorientation towards a better understanding of the temporal dimension in communication processes, resting on the exploding supply of digital trace data and the technical advances in analytical approaches.
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    Agenda formation and prediction of voting tendencies for European parliament election using textual, social and network features
    (Springer, 2024) Shahi, Gautam Kishore; Basyurt, Ali Sercan; Stieglitz, Stefan; Neuberger, Christoph
    As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.
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    Decoding revision mechanisms in Wikipedia: Collaboration, moderation, and collectivities
    (Sage, 2025) Zhang, Xixuan
    Research on knowledge collaboration in Wikipedia has predominately focused on metadata at the article level or editor-centric analyses, often overlooking the complexities of knowledge collaboration and its contextual dependencies. This study takes a novel, fine-grained approach to investigating revision mechanisms in Wikipedia’s knowledge collaboration. By considering modified sentences as carriers of collective knowledge and spaces in which epistemic power is negotiated, it reconstructs their revision sequences and examines how editorial, contextual, content, and temporal factors shape Wikipedia’s revision dynamics. A total of 140,593 revisions (by 48,643 editors) of 76,525 sentences in 537 Wikipedia articles related to climate change were analyzed using text mining, natural language processing, survival analysis, and meta-analysis. The findings expand our understanding of how epistemic power is negotiated through collective endeavors underlying bureaucratic rules and community moderation in Wikipedia.