An approach for measuring rdf data completeness

International audienc

Making digital history: The impact of digitality on public participation and scholarly practices in historical research

This thesis investigates tow key questions: firstly, how do two broad groups - academic, family and local historians, and the public - evaluate, use, and contribute to digital history resources? And consequently, what impact have digital technologies had on public participation and scholarly practices in historical research? Analysing the impact of design on participant experiences and the reception of digital historiography by demonstrating the value of methods drawn from human-computer interaction, including heuristic evaluation, trace ethnography and semi-structured interviews. This thesis also investigates the relationship between heritage crowdsourcing projects (which ask the public to help with meaningful, inherently rewarding tasks that contribute to a shared, significant goal or research interest related to cultural heritage collections or knowledge) and the development of historical skills and interests. It situates crowdsourcing and citizen history within the broader field of participatory digital history and then focuses on the impact of digitality on the research practices of faculty and community historians. Chapter 1 provides an overview of over 400 digital history projects aimed at engaging the public or collecting, creating or enhancing records about historical materials for scholarly and general audiences. Chapter 2 discusses design factors that may influence the success of crowdsourcing projects. Following this, Chapter 3 explores the ways in which some crowdsourcing projects encourage deeper engagement with history or science, and the role of communities of practice in citizen history. Chapter 4 shifts our focus from public participation to scholarly practices in historical research, presenting the results of interviews conducted with 29 faculty and community historians. Finally, the Conclusion draws together the threads that link public participation and scholarly practices, teasing out the ways in which the practices of discovering, gathering, creating and sharing historical materials and knowledge have been affected by digital methods, tools and resources

Enhancing explainability and scrutability of recommender systems

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modified accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ profiles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for finding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-specific item representations. We evaluate all proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen für maschinelle Empfehlungen erfordert Modelle und Methoden für erklärbare, nachvollziehbare und vertrauenswürdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die Lücke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schließen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein Übermaß an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu filtern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen für deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre früheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. Außerdem können Erklärungen für den Fall, dass unerwünschte Inhalte empfohlen werden, wertvolle Informationen darüber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. • Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen für die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese Erklärungen zeigen Beziehungen zwischen Benutzerprofilen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. • Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen für PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind für Benutzer verständlich, da sie Teilmengen früherer Nutzerinteraktionen darstellen, die für die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu finden. • Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspezifischer Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten

The Design of Creative Crowdwork – From Tools for Empowerment to Platform Capitalism

The thesis investigates the methods used in the contemporary crowdsourcing of creative crowdwork and in particular the succession of conflicting ideas and concepts that led to the development of dedi- cated, profit-oriented, online platforms after 2005 for the outsourcing of cognitive tasks and creative labour to a large and unspecified group of people via open calls on the internet. It traces the historic trajectory of the notion of the crowd as well as the development of tech- nologies for online collaboration, with a focus on the accompanying narratives in the form of a dis- course analysis. One focus of the thesis is the clash between the narrative of the empowerment of the individual user through digital tools and the reinvention of the concept of the crowd as a way to refer to users of online platforms in their aggregate form. The thesis argues that the revivification of the notion of the crowd is indicative of a power shift that has diminished the agency of the individual user and empowered the commercial platform providers who, in turn, take unfair advantage of the crowdworker. The thesis examines the workings and the rhetoric of these platforms by comparing the way they address the masses today with historic notions of the crowd, formed by authors like Gustave Le Bon, Sigmund Freud and Elias Canetti. Today’s practice of crowdwork is also juxtaposed with older, arguably more humanist, visions of distributed online collaboration, collective intelligence, free soft- ware and commons-based peer production. The study is a history of ideas, taking some of the utopian concepts of early online history as a vantage point from which to view current and, at times, dystopian applications of crowdsourced creative labour online. The goal is to better understand the social mech- anisms employed by the platforms to motivate and control the crowds they gather, and to uncover the parameters that define their structure as well as the scope for their potential redesign. At its core, the thesis offers a comparison of Amazon Mechanical Turk (2005), the most prominent and infamous example for so-called microtasking or cognitive piecework, with the design of platforms for contest-based creative crowdwork, in particular with Jovoto (2007) and 99designs (2008). The crowdsourcing of design work is organised in decidedly differently ways to other forms of digital labour and the question is why should that be so? What does this tell us about changes in the practice and commissioning of design and what are its effects on design as a profession? However, the thesis is not just about the crowdsourcing of design work: it is also about the design of crowdsourcing as a system. It is about the ethics of these human-made, contingent social systems that are promoted as the future of work. The question underlying the entire thesis is: can crowdsourcing be designed in a way that is fair and sustainable to all stakeholders? The analysis is based on an extensive study of literature from Design Studies, Media and Cul- ture Studies, Business Studies and Human-Computer Interaction, combined with participant observa- tion within several crowdsourcing platforms for design and a series of interviews with different stake- holders

Wikipedia @ 20

Wikipedia’s first twenty years: how what began as an experiment in collaboration became the world’s most popular reference work. We have been looking things up in Wikipedia for twenty years. What began almost by accident—a wiki attached to a nascent online encyclopedia—has become the world’s most popular reference work. Regarded at first as the scholarly equivalent of a Big Mac, Wikipedia is now known for its reliable sourcing and as a bastion of (mostly) reasoned interaction. How has Wikipedia, built on a model of radical collaboration, remained true to its original mission of “free access to the sum of all human knowledge” when other tech phenomena have devolved into advertising platforms? In this book, scholars, activists, and volunteers reflect on Wikipedia’s first twenty years, revealing connections across disciplines and borders, languages and data, the professional and personal. The contributors consider Wikipedia’s history, the richness of the connections that underpin it, and its founding vision. Their essays look at, among other things, the shift from bewilderment to respect in press coverage of Wikipedia; Wikipedia as “the most important laboratory for social scientific and computing research in history”; and the acknowledgment that “free access” includes not just access to the material but freedom to contribute—that the summation of all human knowledge is biased by who documents it. Contributors Phoebe Ayers, Omer Benjakob, Yochai Benkler, William Beutler, Siko Bouterse, Rebecca Thorndike-Breeze, Amy Carleton, Robert Cummings, LiAnna L. Davis, Siân Evans, Heather Ford, Stephen Harrison, Heather Hart, Benjamin Mako Hill, Dariusz Jemielniak, Brian Keegan, Jackie Koerner, Alexandria Lockett, Jacqueline Mabey, Katherine Maher, Michael Mandiberg, Stephane Coillet-Matillon, Cecelia A. Musselman, Eliza Myrie, Jake Orlowitz, Ian A. Ramjohn, Joseph Reagle, Anasuya Sengupta, Aaron Shaw, Melissa Tamani, Jina Valentine, Matthew Vetter, Adele Vrana, Denny Vrandeči

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Information Reliability on the Social Web - Models and Applications in Intelligent User Interfaces

The Social Web is undergoing continued evolution, changing the paradigm of information production, processing and sharing. Information sources have shifted from institutions to individual users, vastly increasing the amount of information available online. To overcome the information overload problem, modern filtering algorithms have enabled people to find relevant information in efficient ways. However, noisy, false and otherwise useless information remains a problem. We believe that the concept of information reliability needs to be considered along with information relevance to adapt filtering algorithms to today's Social Web. This approach helps to improve information search and discovery and can also improve user experience by communicating aspects of information reliability.This thesis first shows the results of a cross-disciplinary study into perceived reliability by reporting on a novel user experiment. This is followed by a discussion of modeling, validating, and communicating information reliability, including its various definitions across disciplines. A selection of important reliability attributes such as source credibility, competence, influence and timeliness are examined through different case studies. Results show that perceived reliability of information can vary greatly across contexts. Finally, recent studies on visual analytics, including algorithm explanations and interactive interfaces are discussed with respect to their impact on the perception of information reliability in a range of application domains

How Intelligence Can Be a Solution to Consequential World Problems

Intelligence research is mainly concerned with basic science questions; what is the psychometric structure of intelligence? What are the cognitive bases of intelligence? What are the brain-based correlates of intelligence? What does intelligence predict? Such research is needed, but there are also problems larger than those presented in intelligence tests, including problems of today. What is the role of human intelligence in solving consequential real-world problems? Here, leading scholars in the field of intelligence each address one real-world problem—a problem of their choice—and explain how intelligence has been, or could be, essential for a solution

A Design Framework for Engaging Collective Interaction Applications for Mobile Devices

The main objective of this research is to define the conceptual and technological key factors of engaging collective interaction applications for mobile devices. To answer the problem, a throwaway prototyping software development method is utilized to study design issues. Furthermore, a conceptual framework is constructed in accordance with design science activities. This fundamentally exploratory research is a combination of literature review, design and implementation of mobile device based prototypes, as well as empirical humancomputer interaction studies, which were conducted during the period 2008 - 2012. All the applications described in this thesis were developed mainly for research purposes in order to ensure that attention could be focused on the problem statement. The thesis presents the design process of the novel Engaging Collective Interaction (ECI) framework that can be used to design engaging collective interaction applications for mobile devices e.g. for public events and co-creational spaces such as sport events, schools or exhibitions. The building and evaluating phases of design science combine the existing knowledge and the results of the throwaway prototyping approach. Thus, the framework was constructed from the key factors identified of six developed and piloted prototypes. Finally, the framework was used to design and implement a collective sound sensing application in a classroom setting. The evaluation results indicated that the framework offered knowledge to develop a purposeful application. Furthermore, the evolutionary and iterative framework building process combined together with the throwaway prototyping process can be presented as an unseen Dual Process Prototyping (DPP) model. Therefore it is claimed that: 1) ECI can be used to design engaging collective interaction applications for mobile devices. 2) DPP is an appropriate method to build a framework or a model. This research indicates that the key factors of the presented framework are: collaborative control, gamification, playfulness, active spectatorship, continuous sensing, and collective experience. Further, the results supported the assumption that when the focus is more on activity rather than technology, it has a positive impact on the engagement. As a conclusion, this research has shown that a framework for engaging collective interaction applications for mobile devices can be designed (ECI) and it can be utilized to build an appropriate application. In addition, the framework design process can be presented as a novel model (DPP). The framework does not provide a step-by-step guide for designing applications, but it helps to refine the design of successful ones. The overall benefit of the framework is that developers can pay attention to the factors of engaging application at an early stage of design