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

Abstract

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 during a national election. We use three methods to detect the spread of misinformation: analysing unusual spatial and temporal behaviours; detecting known false claims and using these to estimate the total prevalence; and detecting amplifiers through language use. We find that while present, detectable spread of misinformation on Twitter was remarkably low during the election period in Denmark.

Leon Derczynski

Associate professor

My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.