
doi: 10.7802/2434
This repository contains an expert-annotated dataset of 1261 tweets and the corresponding annotation framework from the publication "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse" (https://arxiv.org/abs/2206.07360). The tweets are annotated with three different categories of science-relatedness: (1) Scientific knowledge (scientifically verifiable claims): Tweets that include a claim or a question that could be scientifically verified, (2) Reference to scientific knowledge: Tweets that include at least one reference to scientific knowledge (references can either be direct, e.g., DOI, title of a paper or indirect, e.g., a link to an article that includes a direct reference), and (3) Related to scientific research in general: Tweets that mention a scientific research context (e.g., mention a scientist, scientific research efforts, research findings). Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets.
discourse, twitter, false report, science
discourse, twitter, false report, science
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
