Newsrooms handle information with various sources and formats. Processing this heterogeneous information and making sense out of it is key to news production and dissemination steps. With the proliferation of Large Language Models, there is renewed interest and scope in applying knowledge representations to effectively handle and utilize such diverse information. The synergy between LLMs and knowledge graphs, for example, offer potential to build knowledge representations from textual data in a retrieval-friendly way, while minimising hallucinations or factually-incorrect responses. Integrating such techniques in the newsroom workflow, however, remains challenging. Discussions about AI in journalism often focus on the automated generation of news reports to various capacities, whereas its application to knowledge, the most fundamental tool in a newsroom, is often overlooked.
In this exciting backdrop of emerging solutions, the first edition of the workshop on “Knowledge Management in Newsrooms”, in conjunction with the 24th International Conference on Knowledge Engineering and Knowledge Management (EKAW-24), Amsterdam, presents a platform for researchers and practitioners working with the knowledge representations in the domain of journalism.
We welcome contributions describing novel ideas or applications demonstrating the adoption of knowledge management tools and techniques in newsroom tasks such as (but not limited to), information gathering, news production, sensemaking, news writing tools, recommendation models and news audience interaction. The authors are particularly encouraged to reflect on the application of their ideas or solutions in real-world newsroom contexts. The contributions can also cover the ethical considerations related to developing and adopting these technologies in newsrooms, concerns about bias and misinformation, or the implications for journalistic integrity and the role of journalists in such an AI-augmented environment.
Submissions to workshops must be original. Papers that have been previously published or are under review for another journal, conference or workshop must not be considered for publication.
Papers must be submitted in PDF format according to the CEUR-WS template published in the CEUR-WS guidelines. Long papers should be between 10 and 15 pages, while short papers between 5 and 9 pages, including references. Workshop papers must be self-contained and in English.
The publication of the papers from the K-MiN workshop within a single volume of adjunct proceedings on CEUR-WS is under discussion. This will be confirmed soon.
At least one author of each accepted workshop paper has to register for the main conference EKAW-2024. Please note that workshop attendance is only granted to registered conference participants.
The workshop proceedings will be included in the CEUR joint proceedings for Workshops, Tutorials, Posters and Demos of EKAW 2024. We welcome submissions in short/long form, in PDF format, in alignment with the CEUR-WS template published in the CEUR-WS guidelines.
Information on CEUR can be found here
LaTeX
template available here
Dr. Reshmi Gopalakrishna Pillai
Vrije University Amsterdam
r.pillai(at)vu(dot)nl
https://www.linkedin.com/in/reshmi-g-pillai/
Reshmi is a PostDoc Researcher at the Department of Communication Science in the Vrije University Amsterdam, in the consortium project ‘Towards Responsible AI for Local Journalism’. She focuses on the adoption and implementation of artificial intelligence techniques, specifically natural language processing, in the context of local journalism. Her current research explores the construction of structured knowledge bases in the context of regional news media organizations in the Netherlands. Previously (2019-2023), she was a Lecturer at the Informatics Institute, University of Amsterdam, in the Master's program in Information Studies (Data Science track). She holds a PhD (December 2021) from the University of Wolverhampton for her thesis on the expressions of psychological stress in tweets.
Dr. Laurence Dierickx
University of Bergen / Université Libre de Bruxelles
laurence.dierickx(at)uib(dot)no /
laurence.dierickx(at)ulb(dot)be
https://ohmybox.info
Laurence Dierickx is a postdoctoral researcher at the Department of Information Science and Media Studies at the University of Bergen and a fellow researcher at the Digital Democracy Centre at the University of Southern Denmark. Her research focuses on AI and generative AI in fact-checking, ranging from integration into professional practices to risk mitigation strategies. She also teaches digital and data journalism at the Brussels School of Journalism (ULB). She holds a Master's degree in Sciences and Technologies of Information and Communication and a PhD from the Université Libre de Bruxelles, where her thesis explored automated news production through a socio-technical lens. Her interdisciplinary approach advances the understanding of AI-driven journalism and its ethical implications through ethics, data-centric approaches and human-computer interactions.
Dr. Hannes Cools, University of Amsterdam
Prof. Dr. Antske Fokkens, Vrije University Amsterdam
Dr. Marc Gallofré Ocaña, University of Bergen
Dr. Leo Leppänen, University of Helsinki
Prof. Dr. Carl-Gustav Lindén, University of Bergen
Dr. Silvia Majo Vazquez, Vrije University of Amsterdam
Nicolas Mattis, Vrije University of Amsterdam
Dr. Subhayan Mukerjee, National University Singapore
Dr. Valeria Resendez, Vrije University of Amsterdam
Dr. Nadja Shaetz, University of Hamburg
Stefanie Sirén-Heikel, University of Helsinki
Prof. Dr. Wouter van Atteveldt, Vrije University Amsterdam
Prof. Dr. Arjen van Dalen, University of Southern Denmark