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Accelerated High-Quality Mutual-Information Based Word Clustering

Detection and Resolution of Rumors and Misinformation with NLP

Maintaining Quality in FEVER Annotation

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

The Rumour Mill: Making the Spread of Misinformation Explicit and Tangible

Misinformation spread presents a technological and social threat to society. With the advance of AI-based language models, automatically generated texts have become difficult to identify and easy to create at scale. We present "The Rumour Mill", a …

Misinformation on Twitter During the Danish National Election: A Case Study

Elections are a time when communication is important in democracies, including over social media. This paper describes a case study of applying NLP to determine the extent to which misinformation and external manipulation were present on Twitter …

Bornholmsk Natural Language Processing: Resources and Tools

This paper introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. This presents an overview of the language and available data, and the …

Joint Rumour Stance and Veracity

The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of …

Political Stance in Danish

The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, …

The Lacunae of Danish Natural Language Processing

Danish is a North Germanic language spoken principally in Denmark, a country with a long tradition of technological and scientific innovation. However, the language has received relatively little attention from a technological perspective. In this …