Digitale Märkte und Öffentlichkeiten auf Plattformen

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Auflistung Digitale Märkte und Öffentlichkeiten auf Plattformen nach Forschungsbereichen "Demokratie – Partizipation – Öffentlichkeit"

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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.
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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.