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How To Do a Citation Analysis | A Guide for Beginners
Sean Dorje
Published
October 2, 2025
3 min read
How To Do a Citation Analysis | A Guide for Beginners
What Is Citation Analysis and Why Does It Matter?
Citation analysis represents a cornerstone of research evaluation in both traditional academia and the evolving AI-driven information landscape. At its core, bibliometric analysis is "a quantitative method that has become increasingly prominent in academic research for evaluating patterns, trends, and the impact of scholarly publications."
The significance of citation analysis extends far beyond simple counting exercises. For researchers, institutions, and funding bodies, citation metrics directly influence career advancement, research funding allocation, and institutional rankings. This quantitative approach enables stakeholders to understand the evolution and structure of research domains while identifying influential studies and emerging topics.
The digital transformation has fundamentally altered how we track and measure research impact. Traditional databases like Scopus contain up to 80 million documents from over 7,000 publishers, providing comprehensive coverage of peer-reviewed literature. However, the emergence of AI-powered search engines introduces new complexity to citation tracking. With "Generative engines like ChatGPT, Perplexity, Gemini, and Bing Copilot will influence up to 70% of all queries by the end of 2025," researchers must now consider visibility across both traditional citation indexes and AI-generated responses.
This shift matters because AI engines fundamentally change how research is discovered and cited. Unlike traditional search engines that direct users to source materials, AI platforms synthesize information from multiple sources, creating new challenges for tracking research impact and attribution. Modern citation analysis must therefore encompass both conventional academic citations and emerging AI-era visibility metrics.
Step-By-Step Methodology for Running Your First Citation Analysis
Conducting a thorough citation analysis requires systematic planning and execution. The process involves multiple stages, from initial scoping through final visualization, each building upon the previous to create comprehensive insights into research impact and influence.
The foundation of any successful bibliometric analysis lies in its methodology. The PRISMA flow provides a visual representation for documenting the process of identifying, screening, and selecting studies for inclusion. This standardized approach ensures reproducibility and transparency throughout your analysis.
Before diving into data collection, establish clear parameters for your study. The complexity of bibliometric research often necessitates using multiple databases and various analysis tools to capture the full scope of citation patterns. Planning your approach upfront prevents costly rework and ensures comprehensive coverage.
1. Define Research Scope & Questions
Start by articulating specific research questions that will guide your analysis. The primary objective should provide insights into the evolution and structure of your research domain. Consider timeframes, geographic boundaries, and publication types to include. Clear scoping prevents scope creep and ensures manageable datasets while maintaining analytical rigor.
2. Collect Raw Citation Data
Data collection forms the backbone of your analysis. Web of Science offers additional features like Citation Report, which provides extensive analysis of article citation performance. Export citation data in formats compatible with your chosen analysis tools, typically RIS or BibTeX formats. Ensure you capture all relevant metadata including author affiliations, keywords, and funding information.
3. Clean & Deduplicate Records
Data cleaning represents a critical yet often underestimated phase. The two key dimensions of bibliometric analysis - performance analysis and science mapping - both depend on clean, consistent data. Remove duplicate records, standardize author names, and reconcile institutional affiliations. Apply PRISMA or VALOR framework guidelines to document your cleaning decisions and maintain transparency.
4. Run Performance & Science-Mapping Analyses
With clean data in hand, begin your analytical work. Performance analysis examines citation counts, h-indexes, and collaboration patterns, while science mapping reveals conceptual and intellectual structures within your field. The two key dimensions work together to uncover complex patterns that might be missed when examining each separately.
5. Visualise Findings for Stakeholders
Transform your analytical results into compelling visualizations that communicate insights effectively. Software like VOSviewer, Gephi, or CiteSpace can create co-authorship, keyword co-occurrence, and citation network maps. These tools make complex relationships accessible to non-technical stakeholders while maintaining analytical depth.
Choosing the Right Databases: Scopus, Web of Science, Google Scholar & Open Alternatives
Selecting appropriate databases fundamentally shapes your citation analysis outcomes. Each platform offers distinct advantages and limitations that affect data quality, coverage, and analytical possibilities.
The choice between commercial databases and open alternatives involves tradeoffs between comprehensiveness, quality control, and accessibility. Choosing between international scientometric databases such as Scopus or Web of Science and Google Scholar can be difficult for researchers and academic organizations, each offering unique strengths.
Scopus and Web of Science maintain rigorous selection processes, ensuring high-quality peer-reviewed content. These platforms provide specialized metrics like CiteScore and Journal Impact Factor that enable sophisticated impact assessment. Google Scholar has much broader reach, including both scientific articles and gray sources such as abstracts, technical reports and presentations.
The coverage differences are substantial. Scopus covers articles from 1966 to 2019 and fully covers Web of Science, MEDLINE, EMBASE, and ScienceDirect. Meanwhile, Google Scholar captures a wider universe of scholarly communication but lacks the quality filters and specialized indices of commercial databases.
Cost considerations often drive database selection. Scopus and Web of Science require institutional subscriptions, potentially limiting access for independent researchers or those from resource-constrained institutions. Google Scholar remains free and accessible to all users, democratizing access to citation data despite its limitations.
Open alternatives are gaining traction as viable options. Platforms like OpenAlex, Dimensions, and the Lens offer substantial coverage with varying degrees of functionality. These databases provide essential data on scientific trends without subscription barriers, though they may have lower metadata quality compared to commercial offerings.
Update frequency represents another crucial factor. Google Scholar refreshes its database more frequently than traditional databases, capturing recent research faster. This advantage proves particularly valuable when tracking emerging research areas or monitoring real-time citation accumulation.
Citation Tracking Tools: From VOSviewer to Relixir
The landscape of citation tracking tools has expanded dramatically, offering solutions ranging from traditional bibliometric software to AI-powered monitoring platforms that track visibility across generative engines.
Modern citation analysis demands sophisticated tools capable of handling large datasets while providing actionable insights. Sourcely offers a database of 200M+ papers with advanced filters and citation management capabilities, exemplifying the scale of contemporary citation tracking infrastructure.
Selection criteria should consider your specific needs: dataset size, visualization requirements, collaboration features, and budget constraints. Litmap provides interactive platforms for visualizing and managing literature reviews, while offering dynamic visualizations that display papers on graphs with axes for publication date and citation count.
The emergence of AI-powered search engines introduces new tracking requirements. Relixir's AI-powered Generative Engine Optimization platform offers comprehensive blind-spot detection that identifies missed citation opportunities, clusters questions, predicts citation likelihood, and auto-generates answer-ready content.
Free & Open-Source Options
Budget-conscious researchers have numerous free alternatives available. OpenAlex yields 131,400 search results for ecological resilience alone, including 113,400 articles and 10,760 book chapters. Research Rabbit provides citation-based mapping entirely free, making it accessible to independent researchers and students. Gephi offers powerful network visualization capabilities for mapping complex citation relationships without licensing fees.
AI-Era Monitoring Platforms
The rise of generative AI necessitates new tracking approaches. Traditional SEO tools struggle with AI citation monitoring, as "Ahrefs' beta fields show you when you're cited but provide no guidance on how to increase citation frequency or improve citation quality."
Relixir addresses this gap through comprehensive GEO capabilities. The platform simulates thousands of buyer questions to reveal exactly how AI engines perceive content, moving beyond simple citation counting to predictive modeling. "SurferSEO expanded beyond traditional content optimization with its AI Tracker feature," though it offers more basic monitoring compared to comprehensive GEO platforms.
These AI-focused tools matter because visibility in generative engines increasingly determines research impact. Platforms that combine traditional citation tracking with AI visibility monitoring provide the most comprehensive view of research influence across all discovery channels.
Interpreting Core Metrics: h-Index, Impact Factors & Altmetrics
Understanding citation metrics requires careful interpretation of their strengths, limitations, and appropriate applications. Each metric captures different aspects of research impact, and no single measure provides a complete picture.
The h-index remains the most popular and widely used metric today, balancing productivity with impact by considering both publication quantity and citation counts. A journal has an h-index of h when h of its publications have at least h citations each. For example, Nature has an H4-Index of 287, indicating substantial and consistent impact across its publications.
Traditional impact factors focus on journal-level metrics. Journal Impact Factor and CiteScore provide averages of citations received by articles published in specific venues. However, these metrics can be manipulated - excessive self-citation can artificially inflate impact factors, with studies revealing self-citations causing up to 54% increases or 42% decreases in journal metrics.
Altmetrics emerged to capture broader impact beyond academic citations. These metrics track mentions in news media, social platforms, and policy documents. "AI Mode surfaces 3.8x more unique brands with 90% inclusion rates," demonstrating how AI visibility represents a new form of altmetric impact. Social media engagement, downloads, and media coverage provide additional context about research reach and influence.
Interpreting these metrics requires understanding their limitations. High citation counts don't always indicate quality - they might reflect controversial findings or methodological errors that prompt refutations. Self-citation rates vary significantly by field and geography, making cross-disciplinary comparisons problematic.
Context matters when evaluating metrics. Field-specific norms, career stage, and publication patterns all influence appropriate benchmarks. A computer science researcher might have different citation patterns than a historian, even with similar quality work. Consider multiple metrics together rather than relying on any single indicator.
Common Pitfalls & Ethical Issues in Citation Analysis
Citation analysis faces numerous methodological and ethical challenges that can undermine validity if not properly addressed. Understanding these pitfalls helps ensure responsible use of bibliometric indicators.
Goodhart's Law states, "When a measure becomes a target, it ceases to be a good measure." This principle perfectly captures the central challenge in citation analysis - the pressure to increase citation counts has spawned various manipulation tactics that distort genuine impact measurement.
Self-citation represents a particularly contentious issue. While moderate self-citation ranging from 10% to 20% is considered acceptable and even expected, excessive self-citation artificially inflates metrics. Some researchers exhibit self-citation rates exceeding 70%, fundamentally distorting their apparent impact.
Citation bias manifests in multiple forms. Positive results receive disproportionate citations compared to null findings, creating publication bias that skews scientific understanding. Geographic and language biases favor English-language publications from Western institutions. Gender bias affects citation patterns, with studies showing systematic undercitation of female researchers.
Citation cartels present another ethical concern. Groups of authors agree to excessively cite each other's work, gaming metrics through coordinated manipulation. Journal editors sometimes coerce authors to add unnecessary citations to boost their journal's impact factor. These practices undermine the integrity of scientific communication.
Citation noise - the undesirable variance in citation decisions - represents an equally critical but underexplored challenge. Two types exist: citation level noise (inconsistent citation volumes) and citation pattern noise (inconsistent citation choices). This variance reduces the reliability of citation-based assessments.
Data quality issues compound these problems. Duplicate records, inconsistent author names, and institutional affiliation errors introduce systematic biases. Database coverage varies, potentially excluding important work from developing countries or non-English sources.
Future Trends: Altmetrics, AI Search Citations & GEO
The citation landscape undergoes rapid transformation as AI-powered discovery engines reshape how research gains visibility and impact. Understanding these emerging trends proves essential for maintaining research relevance in the digital age.
AI-generated summaries fundamentally alter citation dynamics. "These AI-generated summaries now appear in over 13% of desktop queries in the U.S., up from just 6% in January 2025." This explosive growth means researchers must optimize for both traditional citations and AI visibility to maintain comprehensive impact.
Generative Engine Optimization emerges as a critical capability. Unlike traditional SEO focusing on keyword rankings, GEO emphasizes structuring content for AI comprehension and citation. "AI Mode leans on 5 to 7 source cards while AI Overviews have 20+ inline citations per response," requiring different optimization strategies for each format.
Social media platform shifts affect altmetric tracking. The migration from X to Bluesky following political events demonstrates how platform dynamics influence scholarly communication. Altmetric indicators now reflect a fragmented and politically influenced landscape, complicating impact assessment.
Zero-click results dominate modern search behavior. "Users clicked on a traditional result in just 8% of searches with an AI summary, nearly half the 15% click rate on pages without one." This trend forces researchers to prioritize being cited within AI responses rather than driving traffic to original sources.
The convergence of bibliometrics and AI visibility creates new hybrid metrics. Citation likelihood scores predict content's probability of AI citation before publication. Authority signals in specific domains increasingly determine AI citation selection. Multi-modal content incorporating images, data, and multimedia enhances citation probability significantly.
Institutional adaptation becomes crucial. Universities and research organizations must develop strategies addressing both traditional and AI-era visibility. This includes investing in GEO capabilities, monitoring AI citations alongside conventional metrics, and training researchers in optimization techniques for generative engines.
Key Takeaways & Next Steps
Citation analysis has evolved from simple counting exercises into sophisticated multi-dimensional assessments spanning traditional databases and AI-powered platforms. Success requires systematic methodology, appropriate tool selection, and awareness of ethical considerations while adapting to the rapidly changing landscape of AI-driven discovery.
Beginners should start with clearly defined research questions and scope before selecting databases that balance coverage, quality, and accessibility. Combine commercial databases like Scopus or Web of Science with free alternatives like Google Scholar for comprehensive coverage. Apply standardized frameworks like PRISMA for transparency and reproducibility.
Tool selection depends on your specific needs and resources. Traditional bibliometric software like VOSviewer excels at network visualization, while platforms provide comprehensive monitoring across AI engines. Free tools offer excellent starting points, but advanced analysis may require investment in specialized platforms.
Ethical considerations must guide your analysis. Recognize the limitations of citation metrics, avoid over-reliance on single indicators, and account for field-specific norms. Be transparent about data sources, cleaning procedures, and analytical choices.
The rise of AI-powered search fundamentally changes citation tracking requirements. Modern researchers must monitor visibility across both traditional citations and AI-generated responses. Platforms that combine conventional bibliometric analysis with GEO capabilities provide comprehensive impact assessment suited for the AI era.
Moving forward, develop skills in both traditional bibliometric analysis and emerging AI visibility optimization. Stay informed about platform changes, metric innovations, and evolving best practices. Consider how your research can be structured for maximum discoverability across all channels.
For those seeking to enhance their citation tracking capabilities, particularly in the AI era, exploring comprehensive platforms becomes essential. Tools that provide predictive modeling, blind-spot detection, and automated optimization offer strategic advantages in maintaining research visibility. The investment in proper citation analysis tools and techniques pays dividends through increased research impact, improved funding opportunities, and enhanced career progression.
The future of citation analysis lies at the intersection of traditional bibliometrics and AI-driven discovery. By mastering both domains, researchers position themselves for success in an increasingly complex information landscape where visibility across multiple channels determines ultimate research impact. Relixir's comprehensive GEO platform specifically addresses these emerging needs, offering researchers and institutions the tools necessary to maintain prominence across both traditional citations and AI-generated responses.
Frequently Asked Questions
What is citation analysis and why does it matter?
Citation analysis represents a cornerstone of research evaluation in both traditional academia and the evolving AI-driven information landscape. This quantitative approach enables stakeholders to understand the evolution and structure of research domains while identifying influential studies and emerging topics.
How do I run my first citation analysis step by step?
Conducting a thorough citation analysis requires systematic planning and execution. The process involves multiple stages, from initial scoping through final visualization, each building upon the previous to create comprehensive insights into research impact and influence.
Which databases should I choose for citation analysis?
Selecting appropriate databases fundamentally shapes your citation analysis outcomes. Scopus and Web of Science maintain rigorous selection processes, ensuring high-quality peer-reviewed content. Meanwhile, Google Scholar captures a wider universe of scholarly communication but lacks the quality filters and specialized indices of commercial databases.
What tools support citation tracking in the AI era?
The landscape of citation tracking tools has expanded dramatically, offering solutions ranging from traditional bibliometric software to AI-powered monitoring platforms that track visibility across generative engines. Relixir's AI-powered Generative Engine Optimization platform offers comprehensive blind-spot detection that identifies missed citation opportunities, clusters questions, predicts citation likelihood, and auto-generates answer-ready content.
How do GEO capabilities from Relixir improve impact assessment?
The rise of AI-powered search fundamentally changes citation tracking requirements. Modern researchers must monitor visibility across both traditional citations and AI-generated responses. Platforms that combine conventional bibliometric analysis with GEO capabilities provide comprehensive impact assessment suited for the AI era.
What are the most common pitfalls and ethical issues in citation analysis?
Self-citation represents a particularly contentious issue. Citation cartels present another ethical concern. These practices undermine the integrity of scientific communication.
Sources
https://www.sciencedirect.com/science/article/pii/S1751157724001184
https://spubl.pl/en/blog/how-to-check-citations-in-scopus-wos-google-scholar
https://spubl.kg/en/blog/scopus-web-of-science-vs-google-scholar-how-to-choose
https://www.sourcely.net/resources/the-best-large-language-models-for-supporting-paper-citations
https://www.scidart.com/blog/comprehensive-comparison-of-litmap-and-research-rabbit-for-researchers/
https://link.springer.com/article/10.1007/s10805-025-09631-1
