Project Details
Description
NLP: Argumentation mining & propaganda detection
There’s a lot of misleading content around, and identifying it automatically requires solving some big NLP problems: how can we extract and thus automatically evaluate argumentation? What does misleading or malicious argumentation look like from a computational point of view? Identifying manipulative content is possible for humans, but this does not scale to the web; meanwhile, the web is rife with false and deceptive claims. Computational approaches offer the ability to scale up the search for this content without needing extra human effort.
This project investigates basic general methods and models for extracting and modelling argumentation from documents and social media conversations, and for separating good- and poor-faith arguments based on these representations.
There’s a lot of misleading content around, and identifying it automatically requires solving some big NLP problems: how can we extract and thus automatically evaluate argumentation? What does misleading or malicious argumentation look like from a computational point of view? Identifying manipulative content is possible for humans, but this does not scale to the web; meanwhile, the web is rife with false and deceptive claims. Computational approaches offer the ability to scale up the search for this content without needing extra human effort.
This project investigates basic general methods and models for extracting and modelling argumentation from documents and social media conversations, and for separating good- and poor-faith arguments based on these representations.
Status | Active |
---|---|
Effective start/end date | 01/09/2022 → 31/08/2025 |
Collaborative partners
- IT University of Copenhagen
- Aarhus University (lead)
- Alexandra Instituttet A/S
Funding
- Danish Data Science Academy (DDSA): DKK1,800,000.00
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