Identifying News Slant in Crisis Communication Using Artificial Intelligence
Our project applies different text mining and natural language processing methods, including novel approaches based on language modelling and deep learning to develop a new model for identifying slants in online news and, in this way, promote resilience against biased reporting.
NEWS
- Quanitative Research
- Media Slant Dashboard
- Dissemination Events
- Regulary Blogposts
- GitHub Repository
Orsolya RING received her PhD in History from ELTE University of Budapest. She is working in the poltextLAB Project on creation and classification of large-scale newspaper corpora and elaboration of a domain-specific method for Hungarian sentiment analysis applying various machine learning methods. She is also working on the building of large-scale historical text corpora and its analysis by NLP methods in the Research Group Computational Social Science.
ring.orsolya@tk.hu
SUPPORTED BY
The project is co-financed by the Governments of Czechia, Hungary, Poland and Slovakia through Visegrad Grants from International Visegrad Fund. The mission of the fund is to advance ideas for sustainable regional cooperation in Central Europe.
© Centre for Social Sciences | Address: 4 Tóth Kálmán utca, 1097 Budapest, Hungary | Contact: poltextlab@poltextlab.com