Cross-language learning from bots and users to detect vandalism on Wikipedia

Vandalism, the malicious modification of articles, is a serious problem for open access encyclopedias such as Wikipedia. The use of counter-vandalism bots is changing the way Wikipedia identifies and bans vandals, but their contributions are often not considered nor discussed. In this paper, we propose novel text features capturing the invariants of vandalism across five languages to learn and compare the contributions of bots and users in the task of identifying vandalism. We construct computationally efficient features that highlight the contributions of bots and users, and generalize across languages. We evaluate our proposed features through classification performance on revisions of five Wikipedia languages, totaling over 500 million revisions of over nine million articles. As a comparison, we evaluate these features on the small PAN Wikipedia vandalism data sets, used by previous research, which contain approximately 62,000 revisions. We show differences in the performance of our features on the PAN and the full Wikipedia data set. With the appropriate text features, vandalism bots can be effective across different languages while learning from only one language. Our ultimate aim is to build the next generation of vandalism detection bots based on machine learning approaches that can work effectively across many language

Damage Detection and Mitigation in Open Collaboration Applications

Collaborative functionality is changing the way information is amassed, refined, and disseminated in online environments. A subclass of these systems characterized by open collaboration uniquely allow participants to *modify* content with low barriers-to-entry. A prominent example and our case study, English Wikipedia, exemplifies the vulnerabilities: 7%+ of its edits are blatantly unconstructive. Our measurement studies show this damage manifests in novel socio-technical forms, limiting the effectiveness of computational detection strategies from related domains. In turn this has made much mitigation the responsibility of a poorly organized and ill-routed human workforce. We aim to improve all facets of this incident response workflow. Complementing language based solutions we first develop content agnostic predictors of damage. We implicitly glean reputations for system entities and overcome sparse behavioral histories with a spatial reputation model combining evidence from multiple granularity. We also identify simple yet indicative metadata features that capture participatory dynamics and content maturation. When brought to bear over damage corpora our contributions: (1) advance benchmarks over a broad set of security issues ( vandalism ), (2) perform well in the first anti-spam specific approach, and (3) demonstrate their portability over diverse open collaboration use cases. Probabilities generated by our classifiers can also intelligently route human assets using prioritization schemes optimized for capture rate or impact minimization. Organizational primitives are introduced that improve workforce efficiency. The whole of these strategies are then implemented into a tool ( STiki ) that has been used to revert 350,000+ damaging instances from Wikipedia. These uses are analyzed to learn about human aspects of the edit review process, properties including scalability, motivation, and latency. Finally, we conclude by measuring practical impacts of work, discussing how to better integrate our solutions, and revealing outstanding vulnerabilities that speak to research challenges for open collaboration security

Towards the automatic evaluation of stylistic quality of natural texts: constructing a special-­purpose corpus of stylistic edits from the Wikipedia revision history

This thesis proposes an approach to automatic evaluation of the stylistic quality of natural texts through data-driven methods of Natural Language Processing. Advantages of data driven methods and their dependency on the size of training data are discussed. Also the advantages of using Wikipedia as a source for textual data mining are presented. The method in this project crucially involves a program for quick automatic extraction of sentences edited by users from the Wikipedia Revision History. The resulting edits have been compiled in a large-scale corpus of examples of stylistic editing. The complete modular structure of the extraction program is described and its performance is analyzed. Furthermore, the need to separate stylistic edits stylistic edits from factual ones is discussed and a number of Machine Learning classification algorithms for this task are proposed and tested. The program developed in this project was able to process approximately 10% of the whole Russian Wikipedia Revision history (200 gigabytes of textual data) in one month, resulting in the extraction of more than two millions of user edits. The best algorithm for the classification of edits into factual and stylistic ones achieved 86.2% cross-validation accuracy, which is comparable with state-of-the-art performance of similar models described in published papers.Master i Datalingvistikk og språkteknologiMAHF-DASPDASP35

Enriching and validating geographic information on the web

The continuous growth of available data on the World Wide Web has led to an unprecedented amount of available information. However, the enormous variance in data quality and trustworthiness of information sources impairs the great potential of the large amount of vacant information. This observation especially applies to geographic information on the Web, i.e., information describing entities that are located on the Earth’s surface. With the advent of mobile devices, the impact of geographic Web information on our everyday life has substantially grown. The mobile devices have also enabled the creation of novel data sources such as OpenStreetMap (OSM), a collaborative crowd-sourced map providing open cartographic information. Today, we use geographic information in many applications, including routing, location recommendation, or geographic question answering. The processing of geographic Web information yields unique challenges. First, the descriptions of geographic entities on the Web are typically not validated. Since not all Web information sources are trustworthy, the correctness of some geographic Web entities is questionable. Second, geographic information sources on the Web are typically isolated from each other. The missing integration of information sources hinders the efficient use of geographic Web information for many applications. Third, the description of geographic entities is typically incomplete. Depending on the application, missing information is a decisive criterion for (not) using a particular data source. Due to the large scale of the Web, the manual correction of these problems is usually not feasible such that automated approaches are required. In this thesis, we tackle these challenges from three different angles. (i) Validation of geographic Web information: We validate geographic Web information by detecting vandalism in OpenStreetMap, for instance, the replacement of a street name with advertisement. To this end, we present the OVID model for automated vandalism detection in OpenStreetMap. (ii) Enrichment of geographic Web information through integration: We integrate OpenStreetMap with other geographic Web information sources, namely knowledge graphs, by identifying entries corresponding to the same world real-world entities in both data sources. We present the OSM2KG model for automated identity link discovery between OSM and knowledge graphs. (iii) Enrichment of missing information in geographic Web information: We consider semantic annotations of geographic entities on Web pages as an additional data source. We exploit existing annotations of categorical properties of Web entities as training data to enrich missing categorical properties in geographic Web information. For all of the proposed models, we conduct extensive evaluations on real-world datasets. Our experimental results confirm that the proposed solutions reliably outperform existing baselines. Furthermore, we demonstrate the utility of geographic Web Information in two application scenarios. (i) Corpus of geographic entity embeddings: We introduce the GeoVectors corpus, a linked open dataset of ready-to-use embeddings of geographic entities. With GeoVectors, we substantially lower the burden to use geographic data in machine learning applications. (ii) Application to event impact prediction: We employ several geographic Web information sources to predict the impact of public events on road traffic. To this end, we use cartographic, event, and event venue information from the Web.Durch die kontinuierliche Zunahme verfügbarer Daten im World Wide Web, besteht heute eine noch nie da gewesene Menge verfügbarer Informationen. Das große Potential dieser Daten wird jedoch durch hohe Schwankungen in der Datenqualität und in der Vertrauenswürdigkeit der Datenquellen geschmälert. Dies kann vor allem am Beispiel von geografischen Web-Informationen beobachtet werden. Geografische Web-Informationen sind Informationen über Entitäten, die über Koordinaten auf der Erdoberfläche verfügen. Die Relevanz von geografischen Web-Informationen für den Alltag ist durch die Verbreitung von internetfähigen, mobilen Endgeräten, zum Beispiel Smartphones, extrem gestiegen. Weiterhin hat die Verfügbarkeit der mobilen Endgeräte auch zur Erstellung neuartiger Datenquellen wie OpenStreetMap (OSM) geführt. OSM ist eine offene, kollaborative Webkarte, die von Freiwilligen dezentral erstellt wird. Mittlerweile ist die Nutzung geografischer Informationen die Grundlage für eine Vielzahl von Anwendungen, wie zum Beispiel Navigation, Reiseempfehlungen oder geografische Frage-Antwort-Systeme. Bei der Verarbeitung geografischer Web-Informationen müssen einzigartige Herausforderungen berücksichtigt werden. Erstens werden die Beschreibungen geografischer Web-Entitäten typischerweise nicht validiert. Da nicht alle Informationsquellen im Web vertrauenswürdig sind, ist die Korrektheit der Darstellung mancher Web-Entitäten fragwürdig. Zweitens sind Informationsquellen im Web oft voneinander isoliert. Die fehlende Integration von Informationsquellen erschwert die effektive Nutzung von geografischen Web-Information in vielen Anwendungsfällen. Drittens sind die Beschreibungen von geografischen Entitäten typischerweise unvollständig. Je nach Anwendung kann das Fehlen von bestimmten Informationen ein entscheidendes Kriterium für die Nutzung einer Datenquelle sein. Da die Größe des Webs eine manuelle Behebung dieser Probleme nicht zulässt, sind automatisierte Verfahren notwendig. In dieser Arbeit nähern wir uns diesen Herausforderungen von drei verschiedenen Richtungen. (i) Validierung von geografischen Web-Informationen: Wir validieren geografische Web-Informationen, indem wir Vandalismus in OpenStreetMap identifizieren, zum Beispiel das Ersetzen von Straßennamen mit Werbetexten. (ii) Anreicherung von geografischen Web-Information durch Integration: Wir integrieren OpenStreetMap mit anderen Informationsquellen im Web (Wissensgraphen), indem wir Einträge in beiden Informationsquellen identifizieren, die den gleichen Echtwelt-Entitäten entsprechen. (iii) Anreicherung von fehlenden geografischen Informationen: Wir nutzen semantische Annotationen von geografischen Entitäten auf Webseiten als weitere Datenquelle. Wir nutzen existierende Annotationen kategorischer Attribute von Web-Entitäten als Trainingsdaten, um fehlende kategorische Attribute in geografischen Web-Informationen zu ergänzen. Wir führen ausführliche Evaluationen für alle beschriebenen Modelle durch. Die vorgestellten Lösungsansätze erzielen verlässlich bessere Ergebnisse als existierende Ansätze. Weiterhin demonstrieren wir den Nutzen von geografischen Web-Informationen in zwei Anwendungsszenarien. (i) Korpus mit Embeddings von geografischen Entitäten: Wir stellen den GeoVectors-Korpus vor, einen verlinkten, offenen Datensatz mit direkt nutzbaren Embeddings von geografischen Web-Entitäten. Der GeoVectors-Korpus erleichtert die Nutzung von geografischen Daten in Anwendungen von maschinellen Lernen erheblich. (ii) Anwendung zur Prognose von Veranstaltungsauswirkungen: Wir nutzen Karten-, Veranstaltungs- und Veranstaltungsstätten-Daten aus dem Web, um die Auswirkungen von Veranstaltungen auf den Straßenverkehr zu prognostizieren

Detecting vandalism on Wikipedia across multiple languages

Vandalism, the malicious modification or editing of articles, is a serious problem for free and open access online encyclopedias such as Wikipedia. Over the 13 year lifetime of Wikipedia, editors have identified and repaired vandalism in 1.6% of more than 500 million revisions of over 9 million English articles, but smaller manually inspected sets of revisions for research show vandalism may appear in 7% to 11% of all revisions of English Wikipedia articles. The persistent threat of vandalism has led to the development of automated programs (bots) and editing assistance programs to help editors detect and repair vandalism. Research into improving vandalism detection through application of machine learning techniques have shown significant improvements to detection rates of a wider variety of vandalism. However, the focus of research is often only on the English Wikipedia, which has led us to develop a novel research area of cross-language vandalism detection (CLVD). CLVD provides a solution to detecting vandalism across several languages through the development of language-independent machine learning models. These models can identify undetected vandalism cases across languages that may have insufficient identified cases to build learning models. The two main challenges of CLVD are (1) identifying language-independent features of vandalism that are common to multiple languages, and (2) extensibility of vandalism detection models trained in one language to other languages without significant loss in detection rate. In addition, other important challenges of vandalism detection are (3) high detection rate of a variety of known vandalism types, (4) scalability to the size of Wikipedia in the number of revisions, and (5) ability to incorporate and generate multiple types of data that characterise vandalism. In this thesis, we present our research into CLVD onWikipedia, where we identify gaps and problems in existing vandalism detection techniques. To begin our thesis, we introduce the problem of vandalism onWikipedia with motivating examples, and then present a review of the literature. From this review, we identify and address the following research gaps. First, we propose techniques for summarising the user activity of articles and comparing the knowledge coverage of articles across languages. Second, we investigate CLVD using the metadata of article revisions together with article views to learn vandalism models and classify incoming revisions. Third, we propose new text features that are more suitable for CLVD than text features from the literature. Fourth, we propose a novel context-aware vandalism detection technique for sneaky types of vandalism that may not be detectable through constructing features. Finally, to show that our techniques of detecting malicious activities are not limited to Wikipedia, we apply our feature sets to detecting malicious attachments and URLs in spam emails. Overall, our ultimate aim is to build the next generation of vandalism detection bots that can learn and detect vandalism from multiple languages and extend their usefulness to other language editions of Wikipedia

Methods for detecting and mitigating linguistic bias in text corpora

Im Zuge der fortschreitenden Ausbreitung des Webs in alle Aspekte des täglichen Lebens wird Bias in Form von Voreingenommenheit und versteckten Meinungen zu einem zunehmend herausfordernden Problem. Eine weitverbreitete Erscheinungsform ist Bias in Textdaten. Um dem entgegenzuwirken hat die Online-Enzyklopädie Wikipedia das Prinzip des neutralen Standpunkts (Englisch: Neutral Point of View, kurz: NPOV) eingeführt, welcher die Verwendung neutraler Sprache und die Vermeidung von einseitigen oder subjektiven Formulierungen vorschreibt. Während Studien gezeigt haben, dass die Qualität von Wikipedia-Artikel mit der Qualität von Artikeln in klassischen Enzyklopädien vergleichbar ist, zeigt die Forschung gleichzeitig auch, dass Wikipedia anfällig für verschiedene Typen von NPOV-Verletzungen ist. Bias zu identifizieren, kann eine herausfordernde Aufgabe sein, sogar für Menschen, und mit Millionen von Artikeln und einer zurückgehenden Anzahl von Mitwirkenden wird diese Aufgabe zunehmend schwieriger. Wenn Bias nicht eingedämmt wird, kann dies nicht nur zu Polarisierungen und Konflikten zwischen Meinungsgruppen führen, sondern Nutzer auch negativ in ihrer freien Meinungsbildung beeinflussen. Hinzu kommt, dass sich Bias in Texten und in Ground-Truth-Daten negativ auf Machine Learning Modelle, die auf diesen Daten trainiert werden, auswirken kann, was zu diskriminierendem Verhalten von Modellen führen kann. In dieser Arbeit beschäftigen wir uns mit Bias, indem wir uns auf drei zentrale Aspekte konzentrieren: Bias-Inhalte in Form von geschriebenen Aussagen, Bias von Crowdworkern während des Annotierens von Daten und Bias in Word Embeddings Repräsentationen. Wir stellen zwei Ansätze für die Identifizierung von Aussagen mit Bias in Textsammlungen wie Wikipedia vor. Unser auf Features basierender Ansatz verwendet Bag-of-Word Features inklusive einer Liste von Bias-Wörtern, die wir durch das Identifizieren von Clustern von Bias-Wörtern im Vektorraum von Word Embeddings zusammengestellt haben. Unser verbesserter, neuronaler Ansatz verwendet Gated Recurrent Neural Networks, um Kontext-Abhängigkeiten zu erfassen und die Performance des Modells weiter zu verbessern. Unsere Studie zum Thema Crowd Worker Bias deckt Bias-Verhalten von Crowdworkern mit extremen Meinungen zu einem bestimmten Thema auf und zeigt, dass dieses Verhalten die entstehenden Ground-Truth-Label beeinflusst, was wiederum Einfluss auf die Erstellung von Datensätzen für Aufgaben wie Bias Identifizierung oder Sentiment Analysis hat. Wir stellen Ansätze für die Abschwächung von Worker Bias vor, die Bewusstsein unter den Workern erzeugen und das Konzept der sozialen Projektion verwenden. Schließlich beschäftigen wir uns mit dem Problem von Bias in Word Embeddings, indem wir uns auf das Beispiel von variierenden Sentiment-Scores für Namen konzentrieren. Wir zeigen, dass Bias in den Trainingsdaten von den Embeddings erfasst und an nachgelagerte Modelle weitergegeben wird. In diesem Zusammenhang stellen wir einen Debiasing-Ansatz vor, der den Bias-Effekt reduziert und sich positiv auf die produzierten Label eines nachgeschalteten Sentiment Classifiers auswirkt

Where is the science in Wikipedia? Identification and characterization of scientifically supported contents

This study illustrates the challenges of developing a broad Wikipedia thematic landscape. Particularly the limitations of Wikipedia categories in providing an overview of the thematic areas covered in Wikipedia are shown. The use of WikiProjects is presented as a viable although limited alternative, providing interesting classificatory possibilities. The classification proposed here can be useful for further research on Wikipedia as well as for other researchers who want to identify Wikipedia dynamics in a more aggregated and visual way

Controversy Analysis and Detection

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the filter bubble effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple list of 10 links . This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two themes: computational models of controversy, and controversies occurring in neighborhoods of topics. Our broad contributions are: (1) Presenting a theoretical framework for modeling controversy as contention among populations; (2) Constructing the first automated approach to detecting controversy on the web, using a KNN classifier that maps from the web to similar Wikipedia articles; and (3) Proposing a novel controversy detection in Wikipedia by employing a stacked model using a combination of link structure and similarity. We conclude this work by discussing the challenging technical, societal and ethical implications of this emerging research area and proposing avenues for future work