1. The influence of search engine optimization
on Google's results: A multi-dimensional
approach for detecting SEO
Dirk Lewandowski,
Hamburg University of Applied
Sciences, Germany,
dirk.lewandowski@haw-
hamburg.de
Sebastian Sünkler,
Hamburg University of Applied
Sciences, Germany,
sebastian.suenkler@haw-
hamburg.de
Nurce Yagci,
Hamburg University of Applied
Sciences, Germany,
nurce.yagci@haw-
hamburg.de

2. THE INFLUENCE OF SEARCH ENGINE
OPTIMIZATION ON GOOGLE'S RESULTS
A MULTI-DIMENSIONAL APPROACH FOR DETECTING SEO
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021

3. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
OVERVIEW
1. Relevance of Search Engine Optimization (SEO)
2. Identifying SEO Factors
3. Classifying SEO on three datasets
4. Discussion and conclusion
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4. 1. RELEVANCE OF SEARCH ENGINE
OPTIMIZATION

5. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
1. RELEVANCE OF SEARCH ENGINE OPTIMIZATION
(Schultheiß & Lewandowski, 2021)
Users
Search engine
results page
(SERP)
Search engine
optimizers (SEOs)
Paid search
marketers (PSMs)
Search engine
providers
Content providers

6. 2. IDENTIFYING SEO FACTORS

7. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
2. IDENTIFYING SEO FACTORS:
OUR APPROACH
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(Sünkler et al., 2021)

8. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
2. IDENTIFYING SEO FACTORS: PRIORITIZED INDICATORS
(Sünkler, Yagci und Lewandowski 2021)
Tools and Plugins Description Class
SEO Tools Tools that dedicatedly support SEO measures, e.g., Yoast SEO Plugin Definitely optimized
Analytics Tools Tools that are used for website analytics, e.g., Google Analytics Probably optimized
URL Lists
SEO customers Customers of search engine optimization agencies (manually collected; 1,004 items) Definitely optimized
News websites List of news websites (manually collected; 1,203 items) Definitely optimized
Websites with ads List of websites showing ads (manually collected; 325 items) Definitely optimized
Online shops List of websites (manually collected; 178 items) Probably optimized
Business websites List of business websites (manually collected; 72 items) Probably optimized
Not optimized List of websites know not to be optimized (manually collected; 1 item) Definitely not optimized
Indicators for SEO
Microdata and schema.org Use of microdata or schema.org on a website to define the context of the data, e.g., JSON-LD Definitely optimized
Online advertisements Use of contextual and affiliate marketing on a website, e.g., Google Ads Probably optimized
HTTPS Hypertext Transfer Protocol Secure Probably optimized
SEO in robots.txt SEO indicators in robots.txt of a website, e.g., crawl-delay Probably optimized
Sitemap Use of a sitemap on a website Probably optimized
Viewport Definition of a viewport for a responsive design Probably optimized
Nofollow links Use of tags on the website to instruct search engines to ignore the target of the link for ranking purposes Probably optimized
Canonical links Use of canonical tags on the website to prevent duplicate content issues Probably optimized
Open Graph Tags Usage of open graph tags for previews of content on social media Probably optimized
Pagespeed Loading time of a website < 3s Probably optimized
Description Use of a site description Probably not optimized
Title Use of a site title Probably not optimized

9. 3. CLASSIFYING SEO ON
THREE DATASETS

10. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
3. CLASSIFYING SEO ON THREE DATASETS:
DATASETS
1. Google Trends: queries derived from the Google Trends website
2. Radical right: queries provided by the Medienanstalt Hamburg/Schleswig-Holstein
3. Coronavirus: top queries from Germany from the Microsoft Bing Coronavirus
QuerySet

11. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
3. CLASSIFYING SEO ON THREE DATASETS:
RESULTS FROM THE CLASSIFICATION INTO SEO CLASSES

12. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
3. CLASSIFYING SEO ON THREE DATASETS:
SCORES UP TO POSITION 130
Score up to position 130

13. 4. DISCUSSION AND CONCLUSION

14. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
4. DISCUSSION AND CONCLUSION:
RESULTS
• Our analysis based on three datasets with a total of 256,853 URLs shows that in
Google, a large degree of pages being optimized. This result held for all result
positions.
• No huge differences between the proportion of optimized results on different result
positions.
• Users will be confronted with a high number of SEO-optimized documents, even
when they are willing to consider a large number of results.
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15. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
4. DISCUSSION AND CONCLUSION:
CONCLUSION AND FUTURE WORK
• Our research contributes to better understanding what users get to see on search
engine result pages.
• In future research, we plan to add more search engines, increase datasets in terms
of size and diversity, and add more factors to our model.
• Bringing together the different influences on the search result pages (i.e., through
SEO, paid search marketing, and search engine providers’ self-interests) to measure
how these influence user selection behavior and knowledge acquisition through search
engines.
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16. THANK YOU FOR YOUR ATTENTION!
https://searchstudies.org/
Dirk Lewandowski
https://searchstudies.org/team/
dirk-lewandowski/
Sebastian Sünkler
https://searchstudies.org/team/
sebastian-suenkler/
Nurce Yagci
https://searchstudies.org/team/
nurce-yagci/
ACKNOWLEDGMENTS
This work is funded by the German Research Foundation
(DFG - Deutsche Forschungsgemeinschaft), grant number
417552432.

17. WebSci '21: 13th ACM Web Science Conference 2021, Virtual Event, United Kingdom, June 2021
Dirk Lewandowski, Sebastian Sünkler & Nurce Yacgi
REFERENCES
Sebastian Schultheiß and Dirk Lewandowski. 2021. Misplaced trust? The relationship between trust, ability to identify
commercially influenced results and search engine preference. Journal of Information Science.
https://doi.org/10.1177/01655515211014157
Sebastian Sünkler, Nurce Yagci and Dirk Lewandowski. 2021. Development and software implementation of a preliminary
model to identify the probability of search engine optimization on webpages. Hamburg. https://osf.io/u8d62/
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