  The COVID-19 pandemic has fueled the spread of misinformation on social media
and the Web as a whole. The phenomenon dubbed `infodemic' has taken the
challenges of information veracity and trust to new heights by massively
introducing seemingly scientific and technical elements into misleading
content. Despite the existing body of work on modeling and predicting
misinformation, the coverage of very complex scientific topics with inherent
uncertainty and an evolving set of findings, such as COVID-19, provides many
new challenges that are not easily solved by existing tools. To address these
issues, we introduce SciLander, a method for learning representations of news
sources reporting on science-based topics. SciLander extracts four
heterogeneous indicators for the news sources; two generic indicators that
capture (1) the copying of news stories between sources, and (2) the use of the
same terms to mean different things (i.e., the semantic shift of terms), and
two scientific indicators that capture (1) the usage of jargon and (2) the
stance towards specific citations. We use these indicators as signals of source
agreement, sampling pairs of positive (similar) and negative (dissimilar)
samples, and combine them in a unified framework to train unsupervised news
source embeddings with a triplet margin loss objective. We evaluate our method
on a novel COVID-19 dataset containing nearly 1M news articles from 500 sources
spanning a period of 18 months since the beginning of the pandemic in 2020. Our
results show that the features learned by our model outperform state-of-the-art
baseline methods on the task of news veracity classification. Furthermore, a
clustering analysis suggests that the learned representations encode
information about the reliability, political leaning, and partisanship bias of
these sources.
