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In conjunction with 27th ACM International Conference on Information and Knowledge Management (CIKM 2018) , 22-26 October 2018, Turin, ITALY




  Call For Papers (PDF) 





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 Recommendation

    • News context modeling

    • Deep learning

    • Big data technologies for news streams

    • Practical applications

    • News diversity and filter bubbles

  • News Analytics

    • 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

    • User profiling

  • Evaluation Platforms, Methods and Datasets

    • Experiences with evaluation platforms

    • News datasets

    • Evaluation methods

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.


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