The field of science communication and ranking systems is witnessing significant developments, with a growing focus on understanding public perception and improving the accuracy of ranking models. Researchers are exploring new approaches to model public perception, including the use of computational frameworks and large-scale datasets. These efforts aim to provide insights into how individuals interact with scientific information and how to predict public engagement with science. In the area of ranking systems, there is a increasing interest in addressing biases and improving the fairness of ranking models. This includes the development of new methods for learning to rank, such as two-stage counterfactual learning to rank, and techniques for mitigating the effects of logging policies. Noteworthy papers include:

• The introduction of a computational framework for modeling public perception of science, which provides valuable insights into public responses to scientific information.
• The proposal of a two-stage counterfactual learning to rank method, which estimates the joint value of candidate generator and ranker policies offline.
• The development of a Counterfactual Voting Adjustment method, which accounts for position and herding biases in online platforms and promotes fairer assessment of information quality.