2017 Acm Ieee Joint Conference On Digital Libraries Jcdl

JCDL encompasses the many meanings of the term digital libraries, including (but not limited to) new forms of information institutions operational information systems with all manner of digital content new means of selecting, collecting, organizing, and distributing digital content and theoretical models of information media, including document genres and electronic publishing Digital libraries are distinguished from information retrieval systems because they include more types of media, provide additional functionality and services, and include other stages of the information life cycle, from creation through use The intended community for this conference includes those interested in such aspects of digital libraries as infrastructure institutions metadata content services digital preservation system design implementation interface design human computer interaction evaluation of performance evaluation of usability collection development intellectual property etc

JCDL '17: The 17th ACM/IEEE-CS Joint Conference on Digital Libraries Jun 19, 2017-Jun 23, 2017 Toronto, Canada. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Welcome to the 2018 ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) in Fort Worth, Texas! It is our great pleasure to present the proceedings of the 18th JCDL. This year's conference theme -- From Data to Wisdom: Resilient Integration across Societies, Disciplines, and Systems -- reflects the progress of digital libraries into a mature research field. JCDL has always invited a broad range of reporting on research, development, and best practices, ranging across theories, systems, services, and applications in the field. This year's call focused on inviting contributions from many different disciplines (also newcomers and associated disciplines) and different stakeholders (researchers and practitioners), with the intent to showcase the diverse methods and research mix in the DL community. We believe that this goal has been achieved. This year's sessions cover topics about different object types (e.g., text, multimedia), different domains (science, archives), different digital library development stages (collection building, indexing and access, use), and different analysis approaches (citation analysis, topic modelling, linking). The call for papers attracted submissions from 29 countries on four continents. The program committee reviewed and accepted 26 full research papers (from 71 reviewed), 13 short research papers (from 38 reviewed), 4 tutorials, 5 workshops (from 7 reviewed), 45 posters and 3 demonstrations (from 68 reviewed). The doctoral consortium, which looks to assist and mentor young scholars in the investigation and research of digital libraries, received 20 submissions and accepted 11 for presentation at JCDL. This proceedings volume contains the full text of the papers, as well as abstracts of the keynotes, tutorials, workshops, panels, demonstrations, and posters. All paper submissions went through a rigorous reviewing process with three individual reviewers on each paper and a meta-review by a fourth expert from the DL community, which prepared the discussion for the program committee meeting in February 2018. With over 160 PC committee members from three continents, the program committee met virtually to discuss all submitted papers and the conference schedule. Posters and demos were accepted in two rounds of submissions: first, in an open, public call, as well as a second, invitation-only round for converting longer submissions into poster form. As in the past, we will be awarding three honors: the Vannevar Bush Best Paper Award, the Best Student Paper Award, and the Best Poster/Demo Award. During the opening session of the conference, the nominees for the two Best Paper Awards will be announced. The prizes will be presented at the banquet. We hope you will be inspired by the high quality and creativity of these award-winning papers.

This open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art in the other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint.