Changing Topic Bias in Biomedical Science Maps by Linking Documents Through Alternative Data Sources: Policy Documents, Patents, Authors, Facebook, and Twitter

Abstract Purpose Traditional science maps cluster documents into topics but are inherently biased toward clustering certain topics over others. This study investigates the extent to which topic bias can be influenced through the selection of data sources used to construct document networks. Design/methodology/approach We evaluate the clustering effectiveness of several topic categories using document networks constructed from two traditional science mapping sources (citations and text similarity) and six non-traditional data sources (policy documents, patent families, document authors, Facebook users, Twitter users, and Twitter conversations). Each source is evaluated both independently and in combination with a text similarity network. Findings Different data sources favor different kinds of topics. Facebook users favor health issues, patent families favor biotechnology topics, policy documents favor government and social issues, Twitter conversations favor food topics, Twitter users favor nursing topics, and document authors favor geographical entities. These findings demonstrate that topic bias can be systematically influenced through data source selection. Research limitations The study focuses on biomedical publications and is limited to the topic categories and data sources examined. Additional domains, data sources, and source combination methods may exhibit different patterns of topic bias. Practical implications The ability to influence topic bias through data source selection opens up the possibility of creating science maps tailored to different information needs. The reported source-specific biases can support the design of science maps optimized for particular users, tasks, or domains. Originality/value This study provides one of the first large-scale investigations of how alternative data sources affect topic emergence in science maps. It introduces an expanded methodology for evaluating topic-level clustering effectiveness and systematically characterizes the topical biases associated with different data sources, providing a foundation for future science map customization.