Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.

One big hope about AI as machine learning improves is that we'll be able to use it for drug discovery -- harnessing the pattern-matching power of algorithms to identify promising drug candidates much faster and more cheaply than human scientists could alone. But we may want to tread cautiously: Any system that is powerful and accurate enough to identify drugs that are safe for humans is inherently a system that will also be good at identifying drugs that are incredibly dangerous for humans. They took a machine learning model they'd trained to find non-toxic drugs, and flipped its directive so it would instead try to find toxic compounds. In less than six hours, the system identified tens of thousands of dangerous compounds, including some very similar to VX nerve gas. Their paper hits on three interests of mine, all of which are essential to keep in mind while reading alarming news like this.

One big hope about AI as machine learning improves is that we'll be able to use it for drug discovery -- harnessing the pattern-matching power of …

In this blog post, Leni Helmes, member of the management board and Senior VP of SEMANTiCS 2019 Premium Sponsor FIZ Karlsruhe talks about the mission of this institution how it is carried out in initiatives such as PatentSemTech, an initiative to build and grow the community around patent data and semantic technologies. FIZ Karlsruhe – Leibniz Institute for Information Infrastructure GmbH is one of the large non-university infrastructure institutions in Germany and member of the Leibniz Association. Our public mission is „…to provide scientific information to scientists and researchers, to develop appropriate products and services and to make them publicly accessible." In this context we understand information infrastructure as the entirety of content, technologies and services that enable knowledge to be generated, disseminated and maintained. Information infrastructure is part of the whole scholarly research infrastructure and is becoming increasingly important in the course of the digital transformation.