In conjunction with RecSys 2021.

 


 

 










The workshop addresses technical, societal, and ethical questions related to news analytics. Publishers increasingly rely on automation to supply information to readers. Conversely, readers face information overload as more and more stories become easily accessible. Both trends combine to create an “attention economy.” Historically, editors functioned as gatekeepers to information. Increasingly, publishers replace editors with news recommender systems which also provide a personalized selection of articles. These systems tailor news feeds to readers’ interests by exploiting patterns in interaction data. Even though the workshop has a technical focus, we welcome interdisciplinary contributions that shed light on legal, ethical, and societal ramifications of algorithmic news curation. The workshop introduces a more holistic view of news as a particular application domain of machine learning and knowledge discovery.

Publishers increasingly automate content curation and personalization to present the most relevant stories to readers. This task is challenging due to the dynamics of the news eco-system and lack of information concerning readers’ preferences and interests. Besides, ethical and legal issues emerge from recent trends such as deliberate misinformation campaigns and ignoring privacy regulations. This workshop invites contributions in the realm of news recommendation and analytics. In addition to technical papers, we welcome interdisciplinary submissions. Topics of interests include but are not limited to:

  • News Personalization

    • Context-aware news recommender systems

    • News recommendation in social media

    • Multi-modal news recommendation

    • User behavior analysis and user interest modeling in the news domain

    • User modeling and user profiling

    • Applications of data mining for personalized search and navigation

    • Personalized news user interface and visualization

    • Diversity and multiperspectivity in news personalization and recommendation

  • News Analytics

    • News semantics and ontologies

    • Adaptive and personalized news summarization, categorization, and opinion mining

    • Social Graph and heterogeneous network analysis 

    • User segmentation and community discovery

    • Big data technologies for news streams

    • News framing research

    • Automated news generation 

    • News political leaning and tone

    • News trends and evolution

  • Psychological, Societal, and Ethical Aspects of News Personalization Systems

    • Privacy and security issues

    • Clickbait, fake news, and misinformation detection

    • Diversity and fairness of news search/recommendation

    • Bias in online news

    • Transparency and explainability

    •  Emotion and cognition in news reception