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Twenty-one source documents, graded by what they support

Lewandowski’s classifier labels 29.0% of 256,853 sampled Google result URLs probably optimized, retaining uncertainty (Lewandowski et al., 2021, Figure 1).

The supplied collection contains 21 physical PDF documents, with duplicated editions and multi-article volumes. It does not contain 21 independent empirical studies. The inventory below counts files; the grading tables separate the works and uses inside them.

Our grades describe what a source can support in this section. They are not a ranking of authors, a declaration about an entire journal or an allegation of misconduct. You can find a useful historical quotation in a book whose unsourced traffic figures we would not repeat.

The evidence standard defines the scale. Grade A permits a scoped empirical result; B requires its material limit beside the claim; C is used for a documented evidence problem or descriptive framing; D is outside this section's substantive question. Neither A nor B is permission to make a causal claim from an observational design.

What are the 21 physical source documents?

Document in the supplied collection Grade for this section Reason and permitted use
Lewandowski, Sünkler and Yagci, 2021, The influence of search engine optimization on Google's results, ACM WebSci A Transparent large result-sample design and open research materials. Quote its classifications with query, year and classifier limits. Preserve the split between definite, probable and negative classifications. DOI 10.1145/3447535.3462479.
Search Engine Optimization, the edited Future Internet reprint Article-specific The container includes distinct designs. Use the article-level table below rather than assigning the whole volume the strength of its best article.
Strategic Innovative Marketing and Tourism, Springer proceedings, 2024 D overall; selected uses C or B Most chapters address other questions. Relevant reviews and small case studies need individual limits; an imprint does not validate a chapter's numeric claims. ISBN 978-3-031-51038-0.
Google, Search Engine Optimization Starter Guide, 2010 edition B Primary evidence of what Google recommended then. Historical principles can be discussed; present product instructions require current operator documentation.
Ledford, SEO: Search Engine Optimization Bible, second edition, 2009 B for attributed testimony; C for unsupported figures Named interviews are historical voices. Practitioner recollections, keyword-density targets and unattributed industry statistics are not experimental evidence. ISBN 978-0-470-45264-6.
Lovely Professional University, Search Engine Optimization, supplied course text C Teaching material with date-sensitive instructions and visible editorial problems. The supplied file's cover identifies an editor while the publication page names an author; it is not present-day operator documentation.
HubSpot, Introduction to Search Engine Optimization, historical ebook C Vendor educational material. Use as historical context; recover the original source of any behavioral statistic before treating it as evidence.
Anttila, 2021, Research on Search Engine Optimization Strategy for Voice Search, IJEBMR 5(11) C Narrative synthesis with a material answer/query-length mistranslation on p. 9. Useful as a route to originals, not as independent confirmation of them.
Ranjpour, 2024, The Significant Role of SEO in Effective Web Marketing, AMTP Proceedings, article 23 C Narrative discussion with derivative examples. It is not a new causal evaluation of SEO effectiveness. Original record: digitalcommons.georgiasouthern.edu/amtp-proceedings_2024/23.
Vidhate, Jadhav and Gole, 2026, Role of Artificial Intelligence in Improving SEO, AEO, and GEO Strategies, IJNRD C Conceptual account. Its citation-probability and content-age claims need primary support; terminology can be described with attribution. Supplied identifier IJNRD2606149.
Raju, Reddy, Riyaz, Nausheen and Manasa, 2025, Optimizing for the Artificial Intelligence Driven Search Era, IJSREM C Integrated conceptual framework, not a tested outcome study. Its comparison of SEO, AEO and GEO can be attributed as a framing. DOI 10.55041/IJSREM53069.
Usmany et al., 2024, The Effectiveness of Search Engine Optimization in Marketing: A Meta-anlysis Study, COSTING 7(5) C Included-study counts conflict and result labels are not mapped to individual studies. Discuss the auditability problem; do not use its pooled effect as an established result.
Ahmad, Mahdee and Abu Bakar, F1000Research, earlier version-1 PDF C Questionnaire associations are described as causal relationships. The reported correlation is not a percent of brand positioning explained by SEO. DOI 10.12688/f1000research.73382.1.
Ahmad, Mahdee and Abu Bakar, F1000Research, later downloaded version-1 PDF C The same study in another file and review state. It is not an independent replication. Retain the version when quoting the article or its reviewer comments.
Ahmad, Mahdee and Abu Bakar, F1000Research, version-2 PDF, 2024 C Revision and open review do not remove the observational-design boundary. The open reviewer record is useful evidence about interpretation concerns. DOI 10.12688/f1000research.73382.2.
Singh and Kesarwani, 2025, Study on SEO vs. Paid Advertising: Which Strategy Yields Better ROI?, IJRPR 6(4) C The supplied article does not provide an auditable comparative ROI outcome dataset. An editorial prompt fragment on printed p. 4132 is a specific defect, not proof of the authors' intent.
Rane and Parmar, 2023, A Brief Study on Search Engine Optimization Strategies, AJEEE special issue C Descriptive overview and insufficiently traceable references; no primary outcome analysis establishes its general performance assertions.
Ch Bala Raju, 2019, Search Engine Optimization: A Digital Marketing Giant and Need of Time, IJIRMPS 7(2) C Tables appear without a sufficiently documented instrument, sampling setting or chain from the stated design to the claimed conclusion. Supplied identifier IJIRMPS1902026.
Naila R., undated, Research: Introduction to SEO, supplied guide C Prescriptive density advice and a reference section that does not provide conventional, recoverable bibliographic entries for many claims. No experimental ranking rule is accepted from it.
Almukhtar, Mahmoodd and Kareem, 2021, Search Engine Optimization: A Review, Applied Computer Science 17(1) C Narrative discussion does not supply a measured comparative outcome analysis supporting its recommendations. Trace a claimed operator policy to that operator. DOI 10.23743/acs-2021-07.
Bataineh, Qasim, Idris and Abu-AlSondos, 2025, The Evolution of SEO Strategies, Cogent Business & Management B A documented questionnaire and structural model, but the outcome is perceived ranking and engagement. Do not relabel perceptions as observed search positions. DOI 10.1080/23311975.2025.2491678.

The Future Internet articles need separate grades

Article Grade Design boundary and permitted use
Rovira, Codina, Guerrero-Solé and Lopezosa, 2019, Ranking by Relevance and Citation Counts A for the defined historical comparison Scholarly relevance ordering compared with citation-based rank summaries, with accessible data. Explain the median-by-position analysis and search restrictions. One deposited median range omits two query columns; using all 25 changes the affected rounded coefficient from 0.990 to 0.991. The other eight configurations reproduce at three decimals. It is not a study of software citations in AI answers. DOI 10.3390/fi11090202.
Giannakoulopoulos, Konstantinou, Koutsompolis, Pergantis and Varlamis, 2019, Academic Excellence, Website Quality, SEO Performance B Composite researcher-defined quality and audit measures. Their correlation with academic standing is not a measured correlation with search positions or traffic. The printed SEO sign, two-sided significance value and one regression F value do not reproduce from the deposited results; retain those discrepancies beside any disputed figure. DOI 10.3390/fi11110242.
Ziakis, Vlachopoulou, Kyrkoudis and Karagkiozidou, 2019, Important Factors for Improving Google Search Rank C Small exploratory search-result sample and average correlations below the paper's own stated inference threshold. Use as an evidence-reading example, not a ranking prescription. DOI 10.3390/fi11020032.
Giomelakis, Karypidou and Veglis, 2019, SEO Inside Newsrooms C for generalized effects; descriptive context only Interviews with selected Greek newsroom practitioners describe their work. They do not identify the causal effect of SEO on traffic or represent newsrooms generally. DOI 10.3390/fi11120261.
Karyotakis, Lamprou, Kiourexidou and Antonopoulos, 2019, SEO Practices: A Study about the Way News Websites Allow Users to Comment C for SEO-effect claims A walkthrough records commenting facilities on selected news websites. Those counts do not measure a ranking or citation benefit from comments. DOI 10.3390/fi11090188.
Editorial and collection front matter D as empirical evidence Useful for identifying the collection and its scope. It contributes no independent outcome test simply by introducing the articles.

Which conference chapters address these questions?

Ziakis and Vlachopoulou's AI-and-SEO review, DOI 10.1007/978-3-031-51038-0_43, is C for outcome claims. It organizes a topic and describes a literature-selection process. A taxonomy and a review count are not experiments establishing that a tactic improves present-day AI citation.

Other potentially relevant case material concerns Meslem and Abbaci's traffic-channel comparison; Constantin and collaborators' translation examples; Constantin and Kavoura's museum websites; Mardache and Balasescu's student perceptions; a review-summary application in Mexico; European university chat facilities; and Xanthopoulou and colleagues' corporate-responsibility communication study. These are narrow contextual cases, not a body of evidence for B2B answer-engine effectiveness. Quantitative reuse requires the individual chapter's exact outcome, source date and limitations.

The corporate-responsibility case is chapter 68, DOI 10.1007/978-3-031-51038-0_68. Its website-communication counts may be described as that small case study; they cannot establish an effect on AI answers. The primary identifier distinguishes it from similarly titled material elsewhere in the collection.

A visible defect is a bounded finding

The LPU text says, “As of my knowledge cutoff in September 2021,” in its Core Web Vitals discussion, PDF p. 146. That sentence is a verifiable editorial artifact in the supplied text. It does not tell us who drafted the passage, how the book was produced or whether every instruction elsewhere is wrong.

The same restraint applies to a faulty reference or an untraceable pooled effect. We identify the defect and the claim it prevents us from accepting. We do not infer fraud, an entire journal's quality or a literature-wide failure from a selected reading collection.

The awkward cases remain useful. A survey may reveal how respondents understand SEO while failing to establish ranking effects. A practitioner may identify a practical measurement problem before a later dataset can quantify it. The grade controls the sentence we write, and the precise use can change when better primary evidence becomes available.

What our own data shows

We have no measurement on this point: this selected collection is not a representative sample of the SEO or GEO literature, and its grade distribution cannot estimate the quality of the field. The physical-document count comes from the supplied collection inventory; article and edition distinctions above prevent duplicate files from being counted as independent findings.

Sources

Cite this page

Broadcastwell research team. (2026, October 30). Twenty-one source documents, graded by what they support. Broadcastwell. https://docs.broadcastwell.com/research/evidence/graded-sources/ Published 30 October 2026; updated 30 October 2026.

@online{broadcastwell2026research22,
  author = {{Broadcastwell research team}},
  title = {Twenty-one source documents, graded by what they support},
  year = {2026},
  date = {2026-10-30},
  url = {https://docs.broadcastwell.com/research/evidence/graded-sources/},
  urldate = {2026-10-30},
  note = {Published 2026-10-30; updated 2026-10-30}
}

Prepared by the Broadcastwell research team. Lead: Vikram Patel, AI architect and specialist.

Sources checked 30 October 2026. Independent check: 30 October 2026. This page has not been peer reviewed.

Last updated: 30 October 2026. Next review: 30 January 2027.

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Broadcastwell research. Sources and dates are stated on each page. Prose and derived tables CC BY 4.0, site code MIT. External sources retain their own licenses. Not peer reviewed. Evidence standard.