Use of diverse data sources to control which topics emerge in a science map
This work addresses the problem of biased topic visualization in science mapping for researchers and analysts, offering a method to customize maps, though it is incremental as it builds on existing clustering techniques with new data sources.
The study tackled the inherent topic bias in traditional science maps by exploring how different data sources affect which topics emerge, finding that non-traditional sources like Facebook, patents, and Twitter can favor specific topics such as health issues or biotechnology, enabling tailored map creation.
Traditional science maps visualize topics by clustering documents within a network, but they are inherently biased toward clustering certain topics over others. If these topics could be chosen, then the science maps could be tailored for different needs. In this paper, we explore the extent to which the topic bias of a science map can be changed by choosing different data sources to build the document network. We analyze this by evaluating the clustering effectiveness of several topic categories over two sources that are traditionally used for the creation of science maps (citations and text similarity) and six non-traditional data sources, which we found favor different kinds of topics: Health issues for Facebook users, biotechnology topics for patent families, government and social issues for policy documents, food topics for Twitter conversations, nursing topics for Twitter users, and geographical entities for document authors (the favoring in this latter source was particularly strong). Our results show that diverse data sources can be used to control topic bias, which opens up the possibility of creating science maps tailored for different needs.