A hybrid AI approach for understanding science in online discourse – AI4SCI

AI4Sci develops hybrid AI methods at the intersection of machine learning, distributional semantics and knowledge representation in order to analyze online discourse and in particular scientific controversies taking place on the web with an applications related to the COVID-19 pandemic. Scientific insights form a central part of public discourse, in particular in the context of the COVID-19 pandemic. However, due to the inherent complexity of scientific claims as well as the mechanisms of online platforms, where controversial topics are shown to generate more user interaction, retention and virality, scientific findings tend to be represented in a simplified, decontextualized and often misleading way. In this context, AI4Sci addresses the challenge of providing hybrid AI methods for tracing and interpreting scientific claims in online discourse, as a means to tackle and understand misinformation in society. Progress in areas such as transfer learning and neural NLP have opened up new possibilities for the AI-based interpretation of online discourse. On the other hand, it has been shown that structured knowledge can improve transparency and performance of neural models, while neural language models themselves carry relational knowledge. Building on these insights, the project will develop hybrid AI methods, able to classify and disambiguate online discourse about scientific findings as observable in online news media and the social Web. AI4Sci will build on recent advances in AI at the intersection of neural NLP, distributional semantics and symbolic knowledged to develop methods geared towards the particular problem of extracting and classifying scientific claims about controversial topics together with related contextual information from online discourse and matching them to their respective scientific context. The hybrid methodology of AI4Sci will also contribute to widely recognised issues such as transparency and reproducibility of neural models. Given the very discipline-specific contexts of scientific claims, both in science as well as online discourse, AI4Sci will evaluate methods in two use-cases centered around the COVID-19 pandemic, involving the life sciences as well as the social sciences. The joint expertise of LIRMM (France) and GESIS (Germany) combines backgrounds in symbolic AI and knowledge graphs , with expertise in NLP/NLU. In particular with respect to mining and understanding online discourse on the (social) Web, the two partners complement each other with applications in the context of computational social science (GESIS) and life science (LIRMM). This will advance the AI-related agendas of both organisations and contribute to the AI strategies at the national and international level. The project will build on joint work and GESIS- and LIRMM-hosted corpora, such as knowledge graphs about online discourse, unique Web and social Web crawls as well as scientific data and bibliographic archives, which will accelerate and facilitate the AI4Sci work programme. AI4Sci brings together a highly diverse team of 4 established and 3 young researchers from LIRMM and GESIS, which will be enhanced by two PhD projects.