Attention, sentiments and emotions towards emerging climate technologies on Twitter.

Public perception of emerging climate technologies, such as greenhouse gas removal (GGR) and solar radiation management (SRM), will strongly influence their future development and deployment. Studying perceptions of these technologies with traditional survey methods is challenging, because they are largely unknown to the public. Social media data provides a complementary line of evidence by allowing for retrospective analysis of how individuals share their unsolicited opinions. Our large-scale, comparative study of 1.5 million tweets covers 16 GGR and SRM technologies and uses state-of-the-art deep learning models to show how attention, and expressions of sentiment and emotion developed between 2006 and 2021. We find that in recent years, attention has shifted from general geoengineering themes to specific GGR methods. On the other hand, there is little attention to specific SRM technologies and they often coincide with conspiracy narratives. Sentiments and emotions in GGR tweets tend to be more positive, particularly for methods perceived to be natural, but are more negative when framed in the geoengineering context.

Statistical stopping criteria for automated screening in systematic reviews.

Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents. However, current approaches lack robust stopping criteria, so that reviewers do not know when they have seen all or a certain proportion of relevant documents. This means that such systems are hard to implement in live reviews. This paper introduces a workflow with flexible statistical stopping criteria, which offer real work reductions on the basis of rejecting a hypothesis of having missed a given recall target with a given level of confidence. The stopping criteria are shown on test datasets to achieve a reliable level of recall, while still providing work reductions of on average 17%. Other methods proposed previously are shown to provide inconsistent recall and work reductions across datasets.

On the use of computer-assistance to facilitate systematic mapping.

The volume of published academic research is growing rapidly and this new era of "big literature" poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer-assistance. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.