Beyond Citations: Measuring Idea-level Knowledge Diffusion from Research to Journalism and Policy-making

Despite the importance of social science knowledge for various stakeholders, measuring its diffusion into different domains remains a challenge. This study uses a novel text-based approach to measure the idea-level diffusion of social science knowledge from the research domain to the journalism and policy-making domains. By doing so, we expand the detection of knowledge diffusion beyond the measurements of direct references. Our study focuses on media effects theories as key research ideas in the field of communication science. Using 72,703 documents (2000-2019) from three domains (i.e., research, journalism, and policy-making) that mention these ideas, we count the mentions of these ideas in each domain, estimate their domain-specific contexts, and track and compare differences across domains and over time. Overall, we find that diffusion patterns and dynamics vary considerably between ideas, with some ideas diffusing between other domains, while others do not. Based on the embedding regression approach, we compare contextualized meanings across domains and find that the distances between research and policy are typically larger than between research and journalism. We also find that ideas largely shift roles across domains - from being the theories themselves in research to sense-making in news to applied, administrative use in policy. Over time, we observe semantic convergence mainly for ideas that are practically oriented. Our results characterize the cross-domain diffusion patterns and dynamics of social science knowledge at the idea level, and we discuss the implications for measuring knowledge diffusion beyond citations.

LGDE: Local Graph-based Dictionary Expansion

Expanding a dictionary of pre-selected keywords is crucial for tasks in information retrieval, such as database query and online data collection. Here we propose Local Graph-based Dictionary Expansion (LGDE), a method that uses tools from manifold learning and network science for the data-driven discovery of keywords starting from a seed dictionary. At the heart of LGDE lies the creation of a word similarity graph derived from word embeddings and the application of local community detection based on graph diffusion to discover semantic neighbourhoods of pre-defined seed keywords. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings and can capture word similarities based on paths of semantic association. We validate our method on a corpus of hate speech-related posts from Reddit and Gab and show that LGDE enriches the list of keywords and achieves significantly better performance than threshold methods based on direct word similarities. We further demonstrate the potential of our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on data collected and analysed by domain experts by expanding a conspiracy-related dictionary.