  Distorted science communication harms individuals and society as it can lead
to unhealthy behavior change and decrease trust in scientific institutions.
Given the rapidly increasing volume of science communication in recent years, a
fine-grained understanding of how findings from scientific publications are
reported to the general public, and methods to detect distortions from the
original work automatically, are crucial. Prior work focused on individual
aspects of distortions or worked with unpaired data. In this work, we make
three foundational contributions towards addressing this problem: (1)
annotating 1,600 instances of scientific findings from academic papers paired
with corresponding findings as reported in news articles and tweets wrt. four
characteristics: causality, certainty, generality and sensationalism; (2)
establishing baselines for automatically detecting these characteristics; and
(3) analyzing the prevalence of changes in these characteristics in both
human-annotated and large-scale unlabeled data. Our results show that
scientific findings frequently undergo subtle distortions when reported. Tweets
distort findings more often than science news reports. Detecting fine-grained
distortions automatically poses a challenging task. In our experiments,
fine-tuned task-specific models consistently outperform few-shot LLM prompting.
