Digitale Märkte und Öffentlichkeiten auf Plattformen

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Auflistung Digitale Märkte und Öffentlichkeiten auf Plattformen nach Autor:in "Buehling, Kilian"

Gerade angezeigt 1 - 3 von 3
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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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    LGDE: Local Graph-based Dictionary Expansion
    (2025) Schindler, Juni; Jha, Sneha; Zhang, Xixuan; Buehling, Kilian; Heft, Annett; Barahona, Mauricio
    We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings to capture word similarities based on paths of semantic association, over and above direct pairwise similarities. Exploiting such semantic neighbourhoods enables the expansion of dictionaries of pre-selected keywords, an important step for tasks in information retrieval, such as database queries and online data collection. We validate LGDE on two user-generated English-language corpora and show that LGDE enriches the list of keywords with improved performance relative to methods based on direct word similarities or co-occurrences. We further demonstrate our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on the expansion of a conspiracy-related dictionary from online data collected and analysed by domain experts. Our empirical results and expert user assessment indicate that LGDE expands the seed dictionary with more useful keywords due to the manifold-learning-based similarity network.
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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.