WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks

Wikipedia articles contain multiple links connecting a subject to other pages of the encyclopedia. In Wikipedia parlance, these links are called internal links or wikilinks. We present a complete dataset of the network of internal Wikipedia links for the $9$ largest language editions. The dataset contains yearly snapshots of the network and spans $17$ years, from the creation of Wikipedia in 2001 to March 1st, 2018. While previous work has mostly focused on the complete hyperlink graph which includes also links automatically generated by templates, we parsed each revision of each article to track links appearing in the main text. In this way we obtained a cleaner network, discarding more than half of the links and representing all and only the links intentionally added by editors. We describe in detail how the Wikipedia dumps have been processed and the challenges we have encountered, including the need to handle special pages such as redirects, i.e., alternative article titles. We present descriptive statistics of several snapshots of this network. Finally, we propose several research opportunities that can be explored using this new dataset.Comment: 10 pages, 3 figures, 7 tables, LaTeX. Final camera-ready version accepted at the 13TH International AAAI Conference on Web and Social Media (ICWSM 2019) - Munich (Germany), 11-14 June 201

Conflict and Computation on Wikipedia: a Finite-State Machine Analysis of Editor Interactions

What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more than a decade of cooperation and conflict among its editors. Across a wide range of pages, we discover a bursty war/peace structure where the systems can become trapped, sometimes for months, in a computational subspace associated with significantly higher levels of conflict-tracking "revert" actions. Distinct patterns of behavior characterize the lower-conflict subspace, including tit-for-tat reversion. While a fraction of the transitions between these subspaces are associated with top-down actions taken by administrators, the effects are weak. Surprisingly, we find no statistical signal that transitions are associated with the appearance of particularly anti-social users, and only weak association with significant news events outside the system. These findings are consistent with transitions being driven by decentralized processes with no clear locus of control. Models of belief revision in the presence of a common resource for information-sharing predict the existence of two distinct phases: a disordered high-conflict phase, and a frozen phase with spontaneously-broken symmetry. The bistability we observe empirically may be a consequence of editor turn-over, which drives the system to a critical point between them.Comment: 23 pages, 3 figures. Matches published version. Code for HMM fitting available at http://bit.ly/sfihmm ; time series and derived finite state machines at bit.ly/wiki_hm

From Don\u27t Use It to Let\u27s Edit! : Using Wikipedia to Teach the ARLIS/NA Art, Architecture, and Design Information Competencies

Students can develop the research skills outlined in the ARLIS/NA Art, Architecture, and Design Information Competencies through the process of editing content on Wikipedia. This article situates the competencies for art and design students within the literature on information literacy and the applications of Wikipedia in the college classroom. With a focus on art history, the art information competencies are mapped to the ACRL Framework for Information Literacy for Higher Education and implemented in one-shot instruction sessions. Inspired by the success of the Art+Feminism Wikipedia Edit-a-thons, the author demonstrates how art librarians can build upon the success of these events and outreach initiatives to incorporate Wikipedia into information literacy instruction for art history students

The Case of Wikidata

Since its launch in 2012, Wikidata has grown to become the largest open knowledge base (KB), containing more than 100 million data items and over 6 million registered users. Wikidata serves as the structured data backbone of Wikipedia, addressing data inconsistencies, and adhering to the motto of “serving anyone anywhere in the world,” a vision realized through the diversity of knowledge. Despite being a collaboratively contributed platform, the Wikidata community heavily relies on bots, automated accounts with batch, and speedy editing rights, for a majority of edits. As Wikidata approaches its first decade, the question arises: How close is Wikidata to achieving its vision of becoming a global KB and how diverse is it in serving the global population? This dissertation investigates the current status of Wikidata’s diversity, the role of bot interventions on diversity, and how bots can be leveraged to improve diversity within the context of Wikidata. The methodologies used in this study are mapping study and content analysis, which led to the development of three datasets: 1) Wikidata Research Articles Dataset, covering the literature on Wikidata from its first decade of existence sourced from online databases to inspect its current status; 2) Wikidata Requests-for-Permissions Dataset, based on the pages requesting bot rights on the Wikidata website to explore bots from a community perspective; and 3) Wikidata Revision History Dataset, compiled from the edit history of Wikidata to investigate bot editing behavior and its impact on diversity, all of which are freely available online. The insights gained from the mapping study reveal the growing popularity of Wikidata in the research community and its various application areas, indicative of its progress toward the ultimate goal of reaching the global community. However, there is currently no research addressing the topic of diversity in Wikidata, which could shed light on its capacity to serve a diverse global population. To address this gap, this dissertation proposes a diversity measurement concept that defines diversity in a KB context in terms of variety, balance, and disparity and is capable of assessing diversity in a KB from two main angles: user and data. The application of this concept on the domains and classes of the Wikidata Revision History Dataset exposes imbalanced content distribution across Wikidata domains, which indicates low data diversity in Wikidata domains. Further analysis discloses that bots have been active since the inception of Wikidata, and the community embraces their involvement in content editing tasks, often importing data from Wikipedia, which shows a low diversity of sources in bot edits. Bots and human users engage in similar editing tasks but exhibit distinct editing patterns. The findings of this thesis confirm that bots possess the potential to influence diversity within Wikidata by contributing substantial amounts of data to specific classes and domains, leading to an imbalance. However, this potential can also be harnessed to enhance coverage in classes with limited content and restore balance, thus improving diversity. Hence, this study proposes to enhance diversity through automation and demonstrate the practical implementation of the recommendations using a specific use case. In essence, this research enhances our understanding of diversity in relation to a KB, elucidates the influence of automation on data diversity, and sheds light on diversity improvement within a KB context through the usage of automation.Seit seiner Einführung im Jahr 2012 hat sich Wikidata zu der größten offenen Wissensdatenbank entwickelt, die mehr als 100 Millionen Datenelemente und über 6 Millionen registrierte Benutzer enthält. Wikidata dient als das strukturierte Rückgrat von Wikipedia, indem es Datenunstimmigkeiten angeht und sich dem Motto verschrieben hat, ’jedem überall auf der Welt zu dienen’, eine Vision, die durch die Diversität des Wissens verwirklicht wird. Trotz seiner kooperativen Natur ist die Wikidata-Community in hohem Maße auf Bots, automatisierte Konten mit Batch- Verarbeitung und schnelle Bearbeitungsrechte angewiesen, um die Mehrheit der Bearbeitungen durchzuführen. Da Wikidata seinem ersten Jahrzehnt entgegengeht, stellt sich die Frage: Wie nahe ist Wikidata daran, seine Vision, eine globale Wissensdatenbank zu werden, zu verwirklichen, und wie ausgeprägt ist seine Dienstleistung für die globale Bevölkerung? Diese Dissertation untersucht den aktuellen Status der Diversität von Wikidata, die Rolle von Bot-Eingriffen in Bezug auf Diversität und wie Bots im Kontext von Wikidata zur Verbesserung der Diversität genutzt werden können. Die in dieser Studie verwendeten Methoden sind Mapping-Studie und Inhaltsanalyse, die zur Entwicklung von drei Datensätzen geführt haben: 1) Wikidata Research Articles Dataset, die die Literatur zu Wikidata aus dem ersten Jahrzehnt aus Online-Datenbanken umfasst, um den aktuellen Stand zu untersuchen; 2) Requestfor- Permission Dataset, der auf den Seiten zur Beantragung von Bot-Rechten auf der Wikidata-Website basiert, um Bots aus der Perspektive der Gemeinschaft zu untersuchen; und 3)Wikidata Revision History Dataset, der aus der Bearbeitungshistorie von Wikidata zusammengestellt wurde, um das Bearbeitungsverhalten von Bots zu untersuchen und dessen Auswirkungen auf die Diversität, die alle online frei verfügbar sind. Die Erkenntnisse aus der Mapping-Studie zeigen die wachsende Beliebtheit von Wikidata in der Forschungsgemeinschaft und in verschiedenen Anwendungsbereichen, was auf seinen Fortschritt hin zur letztendlichen Zielsetzung hindeutet, die globale Gemeinschaft zu erreichen. Es gibt jedoch derzeit keine Forschung, die sich mit dem Thema der Diversität in Wikidata befasst und Licht auf seine Fähigkeit werfen könnte, eine vielfältige globale Bevölkerung zu bedienen. Um diese Lücke zu schließen, schlägt diese Dissertation ein Konzept zur Messung der Diversität vor, das die Diversität im Kontext einer Wissensbasis anhand von Vielfalt, Balance und Diskrepanz definiert und in der Lage ist, die Diversität aus zwei Hauptperspektiven zu bewerten: Benutzer und Daten. Die Anwendung dieses Konzepts auf die Bereiche und Klassen des Wikidata Revision History Dataset zeigt eine unausgewogene Verteilung des Inhalts über die Bereiche von Wikidata auf, was auf eine geringe Diversität der Daten in den Bereichen von Wikidata hinweist. Weitere Analysen zeigen, dass Bots seit der Gründung von Wikidata aktiv waren und von der Gemeinschaft inhaltliche Bearbeitungsaufgaben angenommen werden, oft mit Datenimporten aus Wikipedia, was auf eine geringe Diversität der Quellen bei Bot-Bearbeitungen hinweist. Bots und menschliche Benutzer führen ähnliche Bearbeitungsaufgaben aus, zeigen jedoch unterschiedliche Bearbeitungsmuster. Die Ergebnisse dieser Dissertation bestätigen, dass Bots das Potenzial haben, die Diversität in Wikidata zu beeinflussen, indem sie bedeutende Datenmengen zu bestimmten Klassen und Bereichen beitragen, was zu einer Ungleichgewichtung führt. Dieses Potenzial kann jedoch auch genutzt werden, um die Abdeckung in Klassen mit begrenztem Inhalt zu verbessern und das Gleichgewicht wiederherzustellen, um die Diversität zu verbessern. Daher schlägt diese Studie vor, die Diversität durch Automatisierung zu verbessern und die praktische Umsetzung der Empfehlungen anhand eines spezifischen Anwendungsfalls zu demonstrieren. Kurz gesagt trägt diese Forschung dazu bei, unser Verständnis der Diversität im Kontext einer Wissensbasis zu vertiefen, wirft Licht auf den Einfluss von Automatisierung auf die Diversität von Daten und zeigt die Verbesserung der Diversität im Kontext einer Wissensbasis durch die Verwendung von Automatisierung auf

Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic Assessment

A majority of current work in events extraction assumes the static nature of relationships in constant expertise knowledge bases. However, in collaborative environments, such as Wikipedia, information and systems are extraordinarily dynamic over time. In this work, we introduce a new approach for extracting complex structures of events from Wikipedia. We advocate a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language. The evolution of an event is captured successfully primarily based on analyzing the user edits records in Wikipedia. Our work presents a basis for a singular class of evolution-aware entity-primarily based enrichment algorithms and will extensively increase the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem case and conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles in order to show the effectiveness of our proposed answer. Furthermore, we suggest a new event validation automatic method relying on a supervised model to predict the presence of events in a non-annotated corpus. As the extra document source for event validation, we chose the Web due to its ease of accessibility and wide event coverage. Our outcomes display that we are capable of acquiring 70% precision evaluated on a manually annotated corpus. Ultimately, we conduct a comparison of our strategy versus the Current Event Portal of Wikipedia and discover that our proposed WikipEvent along with the usage of Co-References technique may be utilized to provide new and more data on events

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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Automated Detection of Sockpuppet Accounts in Wikipedia

Wikipedia is a free Internet-based encyclopedia that is built and maintained via the open-source collaboration of a community of volunteers. Wikipedia’s purpose is to benefit readers by acting as a widely accessible and free encyclopedia, a comprehensive written synopsis that contains information on all discovered branches of knowledge. The website has millions of pages that are maintained by thousands of volunteer editors. Unfortunately, given its open-editing format, Wikipedia is highly vulnerable to malicious activity, including vandalism, spam, undisclosed paid editing, etc. Malicious users often use sockpuppet accounts to circumvent a block or a ban imposed by Wikipedia administrators on the person’s original account. A sockpuppet is an “online identity used for the purpose of deception.” Usually, several sockpuppet accounts are controlled by a unique individual (or entity) called a puppetmaster. Currently, suspected sockpuppet accounts are manually verified by Wikipedia administrators, which makes the process slow and inefficient. The primary objective of this research is to develop an automated ML and neural-network-based system to recognize the patterns of sockpuppet accounts as early as possible and recommend suspension. We address the problem as a binary classification task and propose a set of new features to capture suspicious behavior that considers user activity and analyzes the contributed content. To comply with this work, we have focused on account-based and content-based features. Our solution was bifurcated into developing a strategy to automatically detect and categorize suspicious edits made by the same author from multiple accounts. We hypothesize that “you can hide behind the screen, but your personality can’t hide.” In addition to the above-mentioned method, we have also encountered the sequential nature of the work. Therefore, we have extended our analysis with a Long Short Term Memory (LSTM) model to track down the sequential pattern of users’ writing styles. Throughout the research, we strive to automate the sockpuppet account detection system and develop tools to help the Wikipedia administration maintain the quality of articles. We tested our system on a dataset we built containing 17K accounts validated as sockpuppets. Experimental results show that our approach achieves an F1 score of 0.82 and outperforms other systems proposed in the literature. We plan to deliver our research to the Wikipedia authorities to integrate it into their existing system