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Scientific topics, claims and resources are increasingly debated as part ofonline discourse, where prominent examples include discourse related toCOVID-19 or climate change. This has led to both significant societal impactand increased interest in scientific online discourse from various disciplines.For instance, communication studies aim at a deeper understanding of biases,quality or spreading pattern of scientific information whereas computationalmethods have been proposed to extract, classify or verify scientific claimsusing NLP and IR techniques. However, research across disciplines currentlysuffers from both a lack of robust definitions of the various forms ofscience-relatedness as well as appropriate ground truth data for distinguishingthem. In this work, we contribute (a) an annotation framework and correspondingdefinitions for different forms of scientific relatedness of online discoursein Tweets, (b) an expert-annotated dataset of 1261 tweets obtained through ourlabeling framework reaching an average Fleiss Kappa $\kappa$ of 0.63, (c) amulti-label classifier trained on our data able to detect science-relatednesswith 89% F1 and also able to detect distinct forms of scientific knowledge(claims, references). With this work we aim to lay the foundation fordeveloping and evaluating robust methods for analysing science as part oflarge-scale online discourse.

SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse