Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, journal, team size, career age, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation count by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with citation count, even after controlling journal impact, team size, and the career age and gender of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. The topic disparity can be used to complement citation count and to recommend papers at the periphery of a discipline because of their less conventional topics.

Identifying emerging health misinformation is a challenge because its manner and type are often unknown. However, many social media users correct misinformation when they encounter it. From this intuition, we implemented a strategy that detects emerging health misinformation by tracking replies that seem to provide accurate information. This strategy is more efficient than keyword-based search in identifying COVID-19 misinformation about antibiotics and a cure. It also reveals the extent to which misinformation has spread on social networks.

National governments take advantage of collective intelligence when conducting foresight processes. They grasp emerging issues through expert reviews as well as public opinions. It raises national agendas and affects policy-making process. Therefore, by examining policy papers which contain societal issues, we can perceive past, current, and future environments. In this study, we exploit policy research database of Republic of Korea, which is a unique source that automatically collects all policy papers written by national research institutes, to extract latent topics and their trends over 10 years through a probabilistic topic model. Detected topics fairly correspond to expert-selected future drivers in national foresight report, implying that public discourse and policy agenda are coupled. We suggest to utilize open government data and text mining methods for building open foresight framework that various actors exchange their opinions on societal issues.