Measuring the true impact of scholarly work is now faster and more comprehensive thanks to new tools designed to track influence beyond simple citation counts. These platforms go beyond identifying who cited a given paper, mapping the broader network of research that follows and is informed by it. This allows researchers and institutions to gain a more nuanced understanding of a work's significance and reach within its field. Ultimately, these influence-watching tools promise to reshape how academic impact is assessed.
For decades, answering that question meant hours of tedious manual work. Researchers would scan reference lists by hand, check citation indexes one record at a time, and piece together a picture of their field's intellectual genealogy with scissors, index cards, and patience. The process was slow, incomplete, and difficult to reproduce.
Then a community of academic researchers librarians, evidence synthesis specialists, and R package developers began building something different. They constructed feed-based systems that could watch citation relationships unfold in something close to real time, turning the scattered trail of academic influence into a navigable map.
The Old Way and the New
Citation chasing has existed as a search technique for as long as academic publishing has had citation indexes. The method is straightforward in concept: start with a known source and follow its references backward through time, or follow its citations forward to see what built on that work.
As the University of Illinois at Urbana-Champaign's Advanced Library Search Strategies guide explains, citation chasing refers to "the process of retracing the research of an author" as a way of finding targeted, relevant research. The guide distinguishes between two directions: backward citation chasing, which uses the Works Cited or References section of an important article to find other related sources, and forward citation chasing (also called cited reference searching), which looks for articles that have cited a previously published work.
What changed in recent years was not the concept but the scale and automation. "Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records," notes the documentation for the citationchaser R package. "This package contains functions to automate this process by making use of the Lens.org API."
That single sentence represents a quiet transformation in how scholars can track influence. Instead of checking one article at a time, researchers can now feed a list of sources into a system and receive back a comprehensive map of every cited and citing record, deduplicated and ready for screening.
Building citationchaser: An R Package for the Systematic Review Crowd
The citationchaser package emerged from the evidence synthesis community, where systematic reviews and meta-analyses demand exhaustive searches. In fields like healthcare, environmental science, and social policy, the rigor of a systematic review depends partly on how thoroughly researchers have traced the literature.
Neal Haddaway, the package's author, designed citationchaser to solve a specific problem: if a systematic review includes 30 or 50 key studies, how does a researcher efficiently extract all the references from those target papers, deduplicate them into a single list, and do the same for every article that cited those papers?
The answer, as Singapore Management University's library guide notes, lies in the Lens.org API. Lens.org aggregates metadata from multiple sources PubMed, PubMed Central, CrossRef, Microsoft Academic Graph, and CORE creating a unified index that citationchaser queries on behalf of the researcher.
This architectural choice matters. more than building its own citation database, citationchaser acts as a connector, translating a researcher's list of seed papers into queries that Lens.org answers from its pooled index. The package provides two primary functions: get_refs for backward citation chasing (finding what a source cites) and get_citation for forward citation chasing (finding what cites a source).
"In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study," the citationchaser documentation explains. "In systematic reviews, this supplementary search technique is known as 'citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles."
The installation process reflects the package's academic utility. Researchers install it from the dedicated R-universe repository with a single command, making the tool accessible to anyone with R installed which, in the evidence synthesis world, is a substantial portion of the research community.
Why Systematic Reviews Pushed the Innovation
The drive to automate citation chasing did not come from pure curiosity about citation networks. It came from the demanding protocols of systematic reviewing, where publication bias and incomplete searching can distort conclusions.
The University of Connecticut's LibGuides on systematic searching for evidence synthesis documents how citation chasing fits into established review standards. The Cochrane Handbook for Systematic Reviews of Interventions requires backward citation chasing of included studies, while forward citation chasing is suggested as an option for reviews on complex and public health interventions. The Collaboration for Environmental Evidence mentions citation chasing but provides no official guidance meaning the field is still standardizing.
This uneven adoption created a gap that tool-builders rushed to fill. When citation chasing is required by methodological protocols but the manual process is prohibitively time-consuming, automation becomes not a convenience but a methodological necessity.
As the UConn guide observes, both backward and forward citation chasing are "excellent resources for finding more relevant sources" but forward chasing is "more challenging" because no single database comprehensively indexes all citations. "There's no way to search comprehensively in a forward direction using a single database or other tool," the guide notes. "What you're seeing is limited to the segment of the published or unpublished literature that database is aware of, which means it's only a subset of all possible citing references."
This limitation is why tools like citationchaser, which aggregate across multiple databases through Lens.org, offer researchers more complete coverage than any single source could provide.
The Feed-Based Architecture: Following Influence Like a Signal
What makes citationchaser and related tools feel almost like surveillance systems for ideas is their feed-based architecture. beyond a one-time search, they create a persistent connection to a citation index that can be re-queried as new citations accumulate.
The Temple University Research Guide on citation chasing frames this capability as part of a broader shift in how researchers approach literature searching. "Using citations from one source to find other sources is a powerful and efficient way to search both for what has been published before and what has been published after," the guide notes. "The tactic of looking both ways is known as chasing citations (also known as forwards and backwards chaining.)"
The guide recommends that researchers start with a known source "particularly a scholarly book or an article recommended or assigned to you" and use it to see who has cited it since publication. This approach inverts the traditional keyword search: instead of asking "what articles match my search terms?" researchers ask "what articles have engaged with this specific intellectual contribution?"
The difference is subtle but significant. Keyword searches capture topical relevance; citation chasing captures intellectual influence. A paper might use completely different terminology from yours but still be engaging with the same foundational work. Citation chasing finds those connections.
For researchers tracking influence in real time, the practical workflow looks like this: identify a set of foundational papers, run them through citationchaser to generate lists of both cited and citing works, screen those results for relevance, add newly discovered relevant papers to the input list, and repeat the process until the citation network stabilizes with no new relevant discoveries.
The Broader Ecosystem: Literature Mapping Tools
Citationchaser is not alone in this space. The Temple University guide to AI tools for research maps a growing ecosystem of literature mapping tools, several of which share citationchaser's feed-based approach to tracking scholarly influence.
Connected Papers searches the Semantic Scholar database and allows users to create visual maps of connected papers based on any single search result. Inciteful offers free literature mapping starting from a seed paper or pair of papers, with transparent documentation of how its ranking algorithms work. Litmaps combines citation-based search with semantic similarity searching, drawing from CrossRef, OpenAlex, and Semantic Scholar. Research Rabbit, described as "always free, non-profit," covers more than 90% of academic databases.
What unites these tools is a shared intuition: scholarly literature forms a network, and that network contains information that keyword searches miss. By visualizing connections, researchers can discover not just papers that match their topic but papers that shaped the conversation their topic is part of.
The Temple guide notes that Aaron Tay, Lead for Data Services at SMU Libraries, "maintains a large, comprehensive list of literature mapping tools on his blog" suggesting that the community of practice around these tools is itself networked, with researchers sharing discoveries about which tools work best for which purposes.
Real-Time Influence: What the Lens.org API Makes Possible
The Lens.org API is the engine that powers citationchaser's real-time capabilities. According to the SMU library documentation, the Lens dataset "consists of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE" a pooling of major academic indexes into a unified citation graph.
The practical benefit is coverage. No single database indexes every publication venue, preprint server, and institutional repository. By aggregating across sources, Lens.org reduces the gap between what exists and what a search can find. The UConn guide notes that "while not comprehensive, [the Lens dataset] is expansive" an honest acknowledgment of limitations paired with an appreciation of scope.
For researchers monitoring influence in something approaching real time, the Lens.org API provides a crucial capability: the ability to re-run a query and see what new citations have accumulated since the last check. This is not quite a push notification system researchers must actively re-query but it means the infrastructure exists for ongoing surveillance of a citation network.
Documenting the Process: PRISMA and Methodological Transparency
One underappreciated aspect of automated citation chasing is the documentation burden it creates. Systematic reviews are expected to be reproducible, which means researchers must record not just what they found but how they found it including which tools they used and how many records each search strategy retrieved.
The UConn guide explicitly addresses this: "If this is part of your systematic searching process, remember to track the numbers of references and citations for your PRISMA flow diagram or whatever reporting tool you're using. Be sure to document and cite your use of automated tools in your methods as well."
This documentation requirement creates a methodological audit trail. Future researchers reviewing a systematic review can see not just the final included studies but the search strategy that identified them including the citation chasing component. Citationchaser supports this by providing output that includes the number of records retrieved, which can be fed directly into PRISMA flow diagrams.
What This Means for PostsNews Readers
For readers researching practitioners, frameworks, and ideas, the emergence of real-time citation tracking tools represents a shift in what "doing research" looks like. The old model find a key paper, manually check its references, hope you found everything relevant is yielding to a more systematic approach where researchers can follow citation networks comprehensively and repeatably.
This matters for anyone evaluating claims about influence. When a framework, book, or lecture is described as "widely cited" or "foundational to the field," citation chasing tools allow you to verify those claims independently. You can start with the cited work, run it through a citation chaser, and see what the actual citation network looks like. Is the work cited primarily by the author's own collaborators, or does it appear across diverse institutions and disciplines? Has it been cited recently, or is its influence historical?
The tools described in this article democratize access to citation intelligence that was previously available only through expensive commercial databases. The citationchaser package is free. Lens.org is freely accessible. Temple, SMU, UConn, and UIUC have published their guides openly. For readers willing to learn the basics of R or navigate a web interface, the infrastructure for real-time scholarly influence tracking is available today.
Limitations and Honest Boundaries
The locked sources are clear about what these tools cannot do. No database comprehensively indexes all citations. The SMU guide notes that tools like citationchaser are "limited to the segment of the published or unpublished literature that database is aware of." The UIUC guide observes that citation chasing is "simply another way of searching databases to find relevant sources" not a replacement for comprehensive keyword searches, but a supplement.
Forward citation chasing faces particular limitations. "There's no way to search comprehensively in a forward direction using a single database or other tool," the UConn guide states flatly. Researchers seeking complete forward coverage should combine multiple tools and databases.
These limitations are not failures they are boundaries. Every research methodology has edges, and the honest tool-builder documents them. Citationchaser's documentation notes that it does not link to GitHub, GitLab, or R-Forge repositories, meaning development information may be harder to locate. The package has been checked with "3 NOTE, 2 OK" meaning some automated checks flagged issues that maintainers have chosen not to resolve or have not yet addressed.
Where to Read Further
Researchers and readers interested in exploring citation chasing tools directly have several entry points:
- The citationchaser package documentation provides installation instructions, function references, and technical details for R users.
- Singapore Management University's guide on Citationchaser as a transparent and efficient systematic searching tool offers context for evidence synthesis workflows.
- The University of Connecticut's systematic searching guide covers citation chasing alongside other supplementary search techniques and includes guidance on PRISMA documentation.
- Temple University's AI tools for research guide maps the broader ecosystem of literature mapping tools beyond citationchaser.
- The University of Illinois at Urbana-Champaign's Advanced Library Search Strategies guide explains citation chasing fundamentals and its relationship to traditional database searching.
For readers wanting a quick start without installing R, the Temple University citation chasing research guide covers the technique using web-based tools including Web of Science and Google Scholar accessible entry points for anyone building their research workflow.
A Tool for Following Ideas
The story of citationchaser and its surrounding ecosystem is ultimately a story about patience and infrastructure. Academic researchers identified a friction point manual citation tracking that was too slow and incomplete for modern systematic review standards and built tools to remove it. They leveraged APIs, pooled databases, and open-source distribution to make those tools widely accessible.
What emerged is a small but meaningful shift in how scholars can track influence. The tools are not magic. They do not replace careful reading, critical evaluation, or domain expertise. But they do something important: they make the invisible network of academic citation visible and navigable. Researchers can now ask, with some confidence, "who has built on this work?" and receive an answer that approaches comprehensiveness.
For readers evaluating ideas, frameworks, and claims in the broader world, these tools offer a glimpse behind the curtain of scholarly influence. A work is not influential simply because its author says so. Citation chasing lets you see the network where the citations flow, who is citing whom, and how ideas propagate across disciplines and time. That is a powerful capability, made available by researchers who took the time to build the infrastructure.