Special issue on News Personalization and Analytics (pp. 921-1085)

Abstract

The rapid development of Internet-based technologies has shifted news consumption from reading physical newspapers to visiting online news websites, digital social networks, news aggregators, and mobile apps. Personalized news delivery services and interfaces alleviate information overload and adapt news content for individuals building on their 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 contributes to these challenges. 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 polarization. These issues need attention from both a technical and social perspective to understand and develop solutions for the societal challenges of news personalization. Lastly, considering the complicated relationships among various news entities and the special properties of news articles, such as short shelf lives, large volume, and high velocity, effective news analysis remains an important and challenging research problem.

@Article{PrefaceToUmuaiSpecialIssue,
  author = 	{Kille, Benjamin
		and Lommatzsch, Andreas
		and Ziegler, J{\"u}rgen
		and {\"O}zg{\"o}bek, {\"O}zlem},
  title = 	{Preface to the special issue on news personalization and analytics},
  journal = {User Modeling and User-Adapted Interaction},
  year = 	{2024},
  publisher = 	{Springer},
  volume = 	{34},
  number = 	{4},
  pages = 	{921--923},
  issn = 	{1573-1391},
  doi = 	{10.1007/s11257-024-09415-z},
  url = 	{https://doi.org/10.1007/s11257-024-09415-z}
}
Authors:
Benjamin Kille, Andreas Lommatzsch, Jürgen Ziegler, Özlem Özgöbek
Category:
Journal
Year:
2024
Location:
User Modeling and User-Adapted Interaction - The Journal of Personalization Research
Link: