Scope of the Special Issue

The rapid development of Internet-based technologies has shifted news consumption models from reading physical newspapers to visiting online news websites, social media platforms, and news aggregators. Personalized news delivery services and interfaces alleviate information overload and adapt news content for individuals building on users' explicit and latent interests. However, there are still many research challenges in this area which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of news recommender systems as well as the big data management for online news services. The highly dynamic and diverse nature of social network platforms adds to these challenges further complexity. Moreover, fake news, disinformation, echo chambers, or biased news framing may hurt the user experience and lead to a poor news ecosystem. Furthermore, news personalization can provide voters with skewed signals featuring own-party bias and affect political actions, resulting in unhealthy outcomes such as increased polarisation. These issues need attention both from a technical and a social perspective to understand and develop solutions for the societal challenges of news personalization. Lastly, considering the complex relationships among various news entities and the special properties of news articles, such as short shelf lives, continuous, large-volume and high-velocity, effective news analysis remains an important and challenging research problem.

This special issue of User Modelling and User-Adapted Interaction aims at presenting recent progress and developments of efficient user modeling and advanced machine learning techniques in various aspects of news personalization and analytics. We invite researchers and practitioners to contribute high-quality articles focusing on the following topics.


Topics
The topics of interest for this special issue 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


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