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.

翻译：传统科学地图通过聚类网络中的文献来可视化主题，但其本质上存在倾向于聚类某些主题而非其他主题的偏差。若能够选择这些主题，则可针对不同需求定制科学地图。本文探索了通过选择不同数据源构建文献网络来改变科学地图主题偏差的程度。我们通过评估若干主题类别在两个传统科学地图数据源（引文与文本相似度）及六个非传统数据源上的聚类效果进行分析。研究发现，不同数据源对不同类型主题有偏向性：Facebook用户关注健康议题，专利族偏向生物技术主题，政策文件侧重政府与社会议题，Twitter讨论涉及食品主题，Twitter用户聚焦护理主题，而文献作者则突出地理实体（其中后者偏向性尤为显著）。结果表明，多样化数据源可用于调控主题偏差，这为创建满足不同需求的定制化科学地图提供了可能。