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9th International Workshop on News Recommendation and Analytics (INRA 2021) will be held in conjunction with RecSys 2021.
Call For Papers
Daily news consumption has a crucial importance where it affects personal beliefs, decision making, political voting and world views in general. The news eco-system has experienced drastic changes over the course of the last decade. News consumption has shifted online and increasingly towards social media. On digital platforms such as news portals and social media where personalization has more importance, news is filtered and ranked even without users’ awareness. Therefore, we encounter challenges such as lack of transparency, diversity, and other ethical considerations while trying to generate the most suitable personalized recommendations for the users.
The 9th International Workshop on News Recommendation and Analytics (INRA 2021 in conjunction with ACM RecSys) invites scholars from diverse disciplines to discuss topics related to news recommender systems, including but not limited to technical, societal, and ethical aspects of news personalization and analytics in the form of scientific and demo papers.
We would also like to invite workshop attendees to submit the extended version of their papers to a forthcoming journal special issue (currently under review) on news personalization and analytics. The special issue will publish the outstanding papers coming out of this workshop in addition to external submissions. More information will be available on our web page as we get more details from our special issue proposal.
Abstract Deadline: 24 July 2021
Submission Deadline: 29 July 2021
Authors' Notification: 21 August 2021
Camera-ready Deadline: 15 September 2021
Workshop Date: 1 October 2021
Topics of interestsTopics of interests for this workshop include but are not limited to:•
News
– Innovative algorithms for news recommendation
– Reader Profiling
– News context and trend modelling
– Big data technologies for news streams
– Practical applications
• News Analytics
– News semantics and ontologies
– News summarisation, classification, and sentiment analysis
– Large-scale news mining and analytics
– News evolution and trends
– News from social media
• Ethical Aspects of News Recommendation
– Detection and analysis of fake news and disinformation
– News diversity and filter bubbles
– Privacy and security in news recommender systems
– Spread mechanisms of disinformation
Submission Types
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
Submission Types
Scientific Papers (Long and short papers)Scientific Papers: We will accept scientific contributions in the form of short and full long papers. Full Long papers must not exceed 16 12 pages and short papers must not exceed eight 6 pages excluding references. The papers should be formatted according to the ACM template with a single column. Please, note that the reviewing process is single-blind. This is to facilitate access for application-oriented papers using data from news organizations.
Demo Papers: We accept papers demonstrating new systems that have been developed within the area of news recommender systemstechnical advances in news personalisation and analytics. Demo papers must be 4–8 pages long and must not exceed eight 6 pages excluding references.
Proceedings
TBAWe are looking forward to your contribution!