The news domain is characterized by a constant flow of unstructured, fragmentary, and unreliable news stories from numerous sources and different perspectives. Quickly finding relevant information challenges readers, who rely on tools to filter the stream of news. The spread of increasing concerns about disinformation coupled with privacy concerns necessitate improving these tools. This workshop addresses primarily news recommender systems and news analytics. As part of news recommendation and analytics, Big Data architectures and large-scale statistical and linguistic techniques are used to extract aggregated knowledge from large news streams and prepare for personalized access to news.
In this workshop we aim to bring researchers, media companies, and practitioners together, in order to exchange ideas about how to create and maintain a trusted and sustainable environment for digital news production and consumption.
Topics of interests for this workshop include but are not limited to:
News context modeling
Big data technologies for news streams
News diversity and filter bubbles
News semantics and ontologies
News from social media
Large-scale news mining and analytics
Fake News and Disinformation
Detection and analysis of disinformation
Spread mechanisms of news disinformation
User Experience Issues
Privacy and security in news recommender systems
Evaluation Platforms, Methods and Datasets
Experiences with evaluation platforms
This year, we also provide the opportunity for the researchers who would like to test their ideas on real world news settings by using our datasets and evaluation platforms.