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Literature Discovery through Citation Chaining and Mapping: About

A quick guide to introduce citation chaining and mapping tools for literature discovery

What are Citation Chaining & Citation Mapping

Citation Chaining and Citation Mapping are two common techniques used to explore scholarly literature and trace the development of ideas and research trends.

 

Citation Chaining

Citation chaining helps you find papers by following citations through a chain of scholarly articles. It helps you trace the development of a research idea or theory over time

Method:

  • usually start with a "perfect" article in hand (also called "seed" article)
  • can be in two directions: Backward searching and forward searching based on this perfect article

Tools:

  • Library's PowerSearch, Google Scholar, Web of Science, Scopus, Semantic Scholar, etc.
  • Learn more from Citation Chaining Tools

Backward searching

  • finds articles in the references within the perfect article to see what prior research it is based on

  • helps you trace classical and foundational studies

 

Forward searching

  • finds articles that have cited the perfect article after its publication
  • helps you trace the latest development on the topic

 

Citation Mapping

Citation mapping uses a more visual approach to find papers through a network of citations and citation analysis between scholarly articles. It helps you identify key publications and authors, and understand the trends of a research field

Method:

  • can start with one or a set of articles or a research topic
  • use an online tool or software to generate citation maps of articles

Tools:

  • CiteSpace, VOSviewer - to get an overview of a research field
  • ResearchRabbit, Connected Papers, Inciteful, Litmaps - to get more papers based on one or a few relevant papers, or a topic search
  • Learn more from Citation Mapping Tools

Citation mapping

  • finds articles through citation network

  • helps you identify key stuides or researchers in a field (ususally represented by larger nodes)

  • helps you understand the trends of a field through clusters of articles (papers on similar topic are spatially closer)

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