Centrality and content creation in networks: the case of German Wikipedia

When contributing content on large online platforms, producers of user-generated content have to decide where to contribute. On a complex and dynamic platform like Wikipedia, this decision is expected to depend on the way the content is organized. We analyse whether the hyperlinks on Wikipedia channel the attention of producers towards more central articles. We observe a sample 7; 635 articles belonging to the category \Economics" on German Wikipedia over 153 weeks and measure their centrality both within this category and in the network of over one million German Wikipedia articles. Our analysis reveals that an additional link from the observed category is associated with around 140 bytes of additional content and with an increase in the number of authors by nearly 0:5. Moreover we observe that the rate of content generation increases notably when previously unlinked articles get connected to the main cluster in the category

Centrality and content creation in networks:The case of economic topics on German wikipedia

We analyze the role of local and global network positions for content contributions to articles belonging to the category “Economy” on the German Wikipedia. Observing a sample of 7635 articles over a period of 153 weeks we measure their centrality both within this category and in the network of over one million Wikipedia articles. Our analysis reveals that an additional link from the observed category is associated with around 140 bytes of additional content and with an increase in the number of authors by 0.5. The relation of links from outside the category to content creation is much weaker. Beyond the econometric analysis, our study sheds light on how the discipline of economics is represented on German Wikipedia. We find non-neoclassical themes to be highly prevalent among the top articles

Networked Knowledge: Approaches to Analyzing Dynamic Networks of Knowledge in Wikis for Mass Collaboration

Contemporary Web 2.0 technologies facilitate the establishment of large online communities of mass collaboration. In shared workspace environments, millions of people interact without knowing each other. The outcome is openly accessible and constantly developing collective knowledge in the form of a more or less organized knowledge base of digital artifacts. With this dissertation I advance a differentiated approach for studying and understanding the principles that underlie knowledge development under these conditions. The work builds on a theoretical consideration of collaborative learning and knowledge building stemming from the interdisciplinary learning sciences and research on computer-supported collaborative learning (CSCL) in particular. Knowledge is understood as substance with static structure that changes over longer periods of time through the activity of community participants in analogy to the progress of scientific ideas in different domains. A complex systems perspective is used to explain knowledge as an emergent phenomenon that amounts to more than the additive collection of individual contributions. This macro level of processes and structures in a community determines to a large extent how new contributions are made and thus how knowledge develops. Based on these conceptualizations, the present dissertation empirically examines large real-life data sets from the online communities Wikipedia and Wikiversity. Knowledge is captured as a network of interconnected articles in different knowledge domains. The topological position of the articles in the networks is evaluated through established network analysis metrics in order to indentify pivotal articles that form the static structural backbone of the collective knowledge. A cross-sectional analysis demonstrates that pivotal articles tend to be written by authors with extensive contribution experience in the community. In a longitudinal study, a mechanism of knowledge development is evidenced according to which pivotal articles attract new knowledge that appears in the network in subsequent periods. Thus, structure and dynamics of collective knowledge are mutually determining. A continuous time approach to studying their interplay is presented using the scientometric method of main path analysis. It consists in evaluating how pivotal the position of each contribution to the knowledge base is relatively to the historical trajectory of knowledge development. This method allows a more immediate analysis of the collaborative process and connects the micro level of individual contributions with the macro level of collective knowledge development. In sum, this dissertation provides a straightforward contribution to the analysis, understanding and facilitation of informal contexts of knowledge production, which become increasingly important also for formal learning policy and practice. My work further makes the relevant novel phenomenon of online mass collaboration accessible for theoretical and empirical consideration in CSCL research and also contributes a valuable methodological approach for the new research filed of learning analytics.Moderne Web 2.0-Technologien ermöglichen das Entstehen von großen Online-Communities der Massenkollaboration. In der virtuellen Umgebung mit gemeinsamen Arbeitsbereichen interagieren Millionen von Menschen, ohne sich gegenseitig zu kennen. Das Ergebnis ist offen zugängliches und sich ständig entwickelndes kollektives Wissen in der Form einer mehr oder weniger organisierten Wissensbasis aus digitalen Artefakten. Mit dieser Dissertation lege ich einen differenzierten Ansatz für die Untersuchung und das Verständnis der Prinzipien vor, die dem Wissensfortschritt unter diesen Bedingungen zugrunde liegen. Die Arbeit baut auf einer theoretischen Betrachtung der kollaborativen Prozesse des Lernens und Wissensproduktion auf, die von den interdisziplinären Learning Sciences und insbesondere von der Forschung im Bereich des computerunterstützen kollaborativen Lernens (CSCL) stammt. Wissen wird als Substanz mit statischer Struktur verstanden, die sich über längere Zeiträume durch die Aktivität von Community-Teilnehmern ändert, in Analogie zum Fortschritt der Ideen in verschiedenen wissenschaftlichen Gebieten. Die Perspektive komplexer Systeme wird eingenommen, um den emergenten Charakter des Wissens zu erklären, der über die Ansammlung einzelner Beiträge hinausgeht. Diese Makroebene der Prozesse und Strukturen in einer Gemeinschaft bestimmt zu einem großen Teil, wie neue Beiträge vorgenommen werden und somit wie sich das Wissen entwickelt. Basierend auf diesen Konzeptualisierungen untersucht die vorliegende Dissertation empirisch große reale Datensätze aus den Online-Communities Wikipedia und Wikiversity. Wissen wird als ein Netzwerk von miteinander verbundenen Artikeln aus verschiedenen Wissensbereichen erfasst. Die topologische Position der Artikel in den Netzen wird durch etablierte Netzwerkanalyse-Metriken bewertet, um die grundlegenden Artikel zu ermitteln, die das statische strukturelle Rückgrat des kollektiven Wissens bilden. Eine Querschnittsanalyse zeigt, dass die grundlegenden Artikel eher von Autoren mit umfangreicher Beitragserfahrung in der Gemeinschaft geschrieben werden. In einer Längsschnittstudie wird ein Mechanismus des Wissensfortschritts belegt, nach dem die grundlegenden Artikel das neue Wissen anlocken, das sich in den Folgeperioden im Netzwerk manifestiert. Dementsprechend bedingen sich Struktur und Dynamik von kollektivem Wissen gegenseitig. Um ihr Zusammenspiel zu untersuchen wird ein Ansatz vorgestellt, der die kontinuierliche Zeit mit der szientometrische Methode der Main Path Anylsis (Hauptpfad-Analyse) berücksichtigt. Es wird ausgewertet, wie grundlegend die Position eines jeden Beitrags zur Wissensbasis ist, in Abhängigkeit von der historischen Zeitschiene des Wissensfortschritts. Diese Methode ermöglicht eine unmittelbare Analyse des kollaborativen Prozesses und verbindet die Mikroebene der einzelnen Beiträge mit der Makroebene der kollektiven Wissensproduktion. Zusammenfassend bietet diese Dissertation einen eindeutigen Beitrag zur Analyse, Verständnis und Förderung von informellen Kontexten der Wissensproduktion, die auch zunehmend für die Politik und Praxis von formellem Lernen an Bedeutung gewinnen. Darüber hinaus macht meine Arbeit das relevante und neuartige Phänomen der Online-Massenkollaboration zugänglich für die theoretische und empirische Forschung in der CSCL und steuert gleichzeitig wertvolle methodische Ansätze für das neue Forschungsfeld der Learning Analytics

Analysing the Entire Wikipedia History with Database Supported Haskell

In this paper we report on our experience of using Database Supported Haskell (DSH) for analysing the entire Wikipedia history. DSH is a novel high-level database query facility allowing for the formulation and efficient execution of queries on nested and ordered collections of data. DSH grew out of a research project on the integration of database querying capabilities into high-level, general-purpose programming languages. It is an emerging trend that querying facilities embedded in general-purpose programming languages are gradually replacing lower-level database languages such as SQL as preferred facilities for querying large-scale database-resident data. We relate this new approach to the current practice which integrates database queries into analysts ’ workflows in a rather ad hoc fashion. This paper would interest early technology adopters interested in new database query languages and practitioners working on large-scale data analysis

Multilevel logistic models of an article receiving new edits.

<p>Multilevel logistic models of an article receiving new edits.</p

Development of the number of articles with new contributions per period.

<p>Development of the number of articles with new contributions per period.</p

Degree distribution in the combined network of psychology and education articles in the seven snapshot years.

<p>In a log-log scale, the colored points display the frequency of articles with a given number of neighbors over the years.</p

Yearly growth in the total number of articles and authors.

<p>Yearly growth in the total number of articles and authors.</p

Multilevel linear models of the change in the edit count of the neighboring articles.

<p>Multilevel linear models of the change in the edit count of the neighboring articles.</p