ABSTRACT
Search engine optimization (SEO) can significantly influence what is shown on the result pages of commercial search engines. However, it is unclear what proportion of (top) results have actually been optimized. We developed a tool that uses a semi-automatic approach to detect, based on a given URL, whether SEO measures were taken. In this multi-dimensional approach, we analyze the HTML code from which we extract information on SEO and analytics tools. Further, we extract SEO indicators on the page level and the website level (e.g., page descriptions and loading time of a website). We amend this approach by using lists of manually classified websites and use machine learning methods to improve the classifier. An analysis based on three datasets with a total of 1,914 queries and 256,853 results shows that a large fraction of pages found in Google is at least probably optimized, which is in line with statements from SEO experts saying that it is tough to gain visibility in search engines without applying SEO techniques.
Supplemental Material
- Muhammad Akram, Imran Sohail, Sikandar Hayat, M Imran Shafi, and Umer Saeed. 2010. Search Engine Optimization Techniques Practiced in Organizations: A Study of Four Organizations. J. Comput. 2, 6 (June 2010), 134–139.Google Scholar
- Judit Bar-Ilan, Kevin Keenoy, Mark Levene, and Eti Yaari. 2009. Presentation bias is significant in determining user preference for search results-A user study. J. Am. Soc. Inf. Sci. Technol. 60, 1 (January 2009), 135–149. https://doi.org/10.1002/asi.20941Google ScholarCross Ref
- Aziz Barbar and Anis Ismail. 2019. Search Engine Optimization (SEO) for Websites. In Proceedings of the 2019 5th International Conference on Computer and Technology Applications, ACM, New York, NY, USA, 51–55. https://doi.org/10.1145/3323933.3324072Google ScholarDigital Library
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In Proceedings of the international conference on Web search and web data mining, Microsoft Research, Cambridge, United Kingdom Microsoft Research, Redmond, United States, 87–94.Google ScholarDigital Library
- Ashwini Dalvi and Riya Saraf. 2019. Inspecting Engineering College Websites for Effective Search Engine Optimization. In 2019 International Conference on Nascent Technologies in Engineering (ICNTE), IEEE, 1–5. https://doi.org/10.1109/ICNTE44896.2019.8945823Google Scholar
- Fernando Diaz. 2016. Worst Practices for Designing Production Information Access Systems. ACM SIGIR Forum 50, 1 (June 2016), 2–11. https://doi.org/10.1145/2964797.2964799Google ScholarDigital Library
- Murray Dick. 2011. Search Engine Optimisation in Uk News Production. Journal. Pract. 5, 4 (August 2011), 462–477. https://doi.org/10.1080/17512786.2010.551020Google Scholar
- Ioannis C Drivas, Damianos P Sakas, Georgios A Giannakopoulos, and Daphne Kyriaki-Manessi. 2020. Big Data Analytics for Search Engine Optimization. Big Data Cogn. Comput. 4, 2 (April 2020), 1–22. https://doi.org/10.3390/bdcc4020005Google Scholar
- Edelman. 2020. Edelman Trust Barometer 2020.Google Scholar
- European Commission. 2016. Special Eurobarometer 447 – Online Platforms. European Commission, Brussels. https://doi.org/10.2759/937517Google Scholar
- Michael P. Evans. 2007. Analysing Google rankings through search engine optimization data. Internet Res. 17, 1 (2007), 21–37. https://doi.org/10.1108/10662240710730470Google ScholarCross Ref
- Andreas Giannakoulopoulos, Nikos Konstantinou, Dimitris Koutsompolis, Minas Pergantis, and Iraklis Varlamis. 2019. Academic excellence,website quality, SEO performance: Is there a correlation? Futur. Internet 11, 11 (2019), 1–25. https://doi.org/10.3390/fi11110242Google ScholarCross Ref
- Dimitrios Giomelakis, Christina Karypidou, and Andreas Veglis. 2019. SEO inside Newsrooms: Reports from the Field. Futur. Internet 11, 12 (December 2019), 261. https://doi.org/10.3390/fi11120261Google ScholarCross Ref
- Dimitrios Giomelakis and Andreas Veglis. 2016. Investigating Search Engine Optimization Factors in Media Websites. Digit. Journal. 4, 3 (April 2016), 379–400. https://doi.org/10.1080/21670811.2015.1046992Google Scholar
- Sharad Goel, Andrei Broder, Evgeniy Gabrilovich, and Bo Pang. 2010. Anatomy of the long tail. In Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10, ACM Press, New York, New York, USA, 201. https://doi.org/10.1145/1718487.1718513Google ScholarDigital Library
- Nadine Höchstötter and Dirk Lewandowski. 2009. What users see – Structures in search engine results pages. Inf. Sci. (Ny). 179, 12 (May 2009), 1796–1812. https://doi.org/10.1016/j.ins.2009.01.028Google ScholarDigital Library
- C D Hoyos, J C Duque, A F Barco, and É Vareilles. 2019. A search engine optimization recommender system. In CEUR Workshop Proceedings, 43–47.Google Scholar
- Thorsten Joachims, Laura Granka, Bing Pan, and Helene Hembrooke. Accurately Interpreting Clickthrough Data as Implicit Feedback.Google Scholar
- Mark T. Keane, Maeve O'Brien, and Barry Smyth. 2008. Are people biased in their use of search engines? Commun. ACM 51, 2 (February 2008), 49–52. https://doi.org/10.1145/1314215.1314224Google ScholarDigital Library
- Diane Kelly and Leif Azzopardi. 2015. How many results per page? In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’15, ACM Press, New York, New York, USA, 183–192. https://doi.org/10.1145/2766462.2767732Google ScholarDigital Library
- Raphael N. Klein, Lisa Beutelspacher, Katharina Hauk, Christina Terp, Denis Anuschewski, Christoph Zensen, Violeta Trkulja, and Katrin Weller. 2009. Informationskompetenz in Zeiten des Web 2.0: Chancen und Herausforderungen im Umgang mit Social Software. Inf. - Wiss. Prax. 60, 3 (2009), 129–142.Google Scholar
- Dirk Lewandowski. 2017. Users’ Understanding of Search Engine Advertisements. J. Inf. Sci. Theory Pract. 5, 4 (2017), 6–25. https://doi.org/10.1633/JISTaP.2017.5.4.1Google Scholar
- Dirk Lewandowski and Yvonne Kammerer. 2020. Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behav. Inf. Technol. (May 2020), 1–31. https://doi.org/10.1080/0144929X.2020.1761450Google Scholar
- Dirk Lewandowski, Friederike Kerkmann, Sandra Rümmele, and Sebastian Sünkler. 2018. An empirical investigation on search engine ad disclosure. J. Assoc. Inf. Sci. Technol. 69, 3 (March 2018), 420–437. https://doi.org/10.1002/asi.23963Google ScholarDigital Library
- Dirk Lewandowski and Sebastian Sünkler. 2013. Representative online study to evaluate the revised commitments proposed by Google on 21 October 2013 as part of EU competition investigation AT.39740-Google: Country comparison report. Hamburg.Google Scholar
- Kai Li, Mei Lin, Zhangxi Lin, and Bo Xing. 2014. Running and chasing - The competition between paid search marketing and search engine optimization. Proc. Annu. Hawaii Int. Conf. Syst. Sci. (2014), 3110–3119. https://doi.org/10.1109/HICSS.2014.640Google ScholarDigital Library
- Zeyang Liu, Yiqun Liu, Ke Zhou, Min Zhang, and Shaoping Ma. 2015. Influence of Vertical Result in Web Search Examination. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’15, ACM Press, New York, New York, USA, 193–202. https://doi.org/10.1145/2766462.2767714Google ScholarDigital Library
- Carlos Lopezosa, Lluís Codina, Javier Díaz-Noci, and José-Antonio Ontalba. 2020. SEO and the digital news media: From the workplace to the classroom. Comunicar 28, 63 (April 2020), 63–72. https://doi.org/10.3916/C63-2020-06Google ScholarCross Ref
- Goran Matošević, Jasminka Dobša, and Dunja Mladenić. 2021. Using machine learning for web page classification in search engine optimization. Futur. Internet 13, 1 (2021), 1–20. https://doi.org/10.3390/fi13010009Google ScholarCross Ref
- TJ McCue. 2018. SEO Industry Approaching $80 Billion But All You Want Is More Web Traffic. forbes.com.Google Scholar
- Mike Moran and Bill Hunt. 2015. Search Engine Marketing, Inc.: Driving Search Traffic to Your Company's Website (Third edit ed.). IBM Press, Upper Saddle River, NJ.Google Scholar
- Lourdes Moreno and Paloma Martinez. 2013. Overlapping factors in search engine optimization and web accessibility. Online Inf. Rev. 37, 4 (2013), 564–580. https://doi.org/10.1108/OIR-04-2012-0063Google ScholarCross Ref
- Ushadi Niranjika and Dinesh Samarasighe. 2019. Exploring the Effectiveness of Search Engine Optimization Tactics for Dynamic Websites in Sri Lanka. In 2019 Moratuwa Engineering Research Conference (MERCon), IEEE, 267–272. https://doi.org/10.1109/MERCon.2019.8818903Google Scholar
- Barış Özkan, Eren Özceylan, Mehmet Kabak, and Metin Dağdeviren. 2020. Evaluating the websites of academic departments through SEO criteria: a hesitant fuzzy linguistic MCDM approach. Springer Netherlands. https://doi.org/10.1007/s10462-019-09681-zGoogle ScholarDigital Library
- Bing Pan, Helene Hembrooke, Thorsten Joachims, Lori Lorigo, Geri Gay, and Laura Granka. 2007. In Google We Trust: Users’ Decisions on Rank, Position, and Relevance. J. Comput. Commun. 12, 3 (April 2007), 801–823. https://doi.org/10.1111/j.1083-6101.2007.00351.xGoogle Scholar
- Philip Petrescu. 2014. Google Organic Click-Through Rates in 2014 - Moz.Google Scholar
- Indra Prawira and Mariko Rizkiansyah. 2018. Search engine optimization in news production online marketing practice in Indonesia online news media. Pertanika J. Soc. Sci. Humanit. 26, T (2018), 263–270.Google Scholar
- Kristen Purcell, Joanna Brenner, and Lee Raine. 2012. Search Engine Use 2012. Washington, DC.Google Scholar
- Joni Salminen, Roope Marttila, Bernard J. Jansen, Juan Corporan, and Tommi Salenius. 2019. Using machine learning to predict ranking of webpages in the gift industry: Factors for search-engine optimization. ACM Int. Conf. Proceeding Ser. (2019). https://doi.org/10.1145/3361570.3361578Google ScholarDigital Library
- Philipp Schaer, Philipp Mayr, Sebastian Sünkler, and Dirk Lewandowski. 2016. How Relevant is the Long Tail? In CLEF 2016, Norbert Fuhr, Paulo Quaresma, Teresa Gonçalves, Birger Larsen, Krisztian Balog, Craig Macdonald, Linda Cappellato and Nicola Ferro (eds.). Springer International Publishing, Cham, 227–233. https://doi.org/10.1007/978-3-319-44564-9_20Google ScholarCross Ref
- Sebastian Schultheiß and Dirk Lewandowski. 2020. “Outside the industry, nobody knows what we do” SEO as seen by search engine optimizers and content providers. J. Doc. (2020). https://doi.org/10.1108/JD-07-2020-0127Google Scholar
- Sebastian Schultheiß and Dirk Lewandowski. 2020. How users’ knowledge of advertisements influences their viewing and selection behavior in search engines. J. Assoc. Inf. Sci. Technol. (September 2020), asi.24410. https://doi.org/10.1002/asi.24410Google ScholarDigital Library
- 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. May 2021. https://doi.org/10.1177/01655515211014157Google ScholarDigital Library
- Sebastian Schultheiß and Dirk Lewandowski. 2021. Expert interviews with stakeholder groups in the context of commercial search engineswithin the SEO Effect project. Retrieved from https://osf.io/5aufr/Google Scholar
- Sebastian Schultheiß, Sebastian Sünkler, and Dirk Lewandowski. 2018. We still trust in google, but less than 10 years ago: An eye-tracking study. Inf. Res. 23, 3 (2018).Google Scholar
- Jenna Pack Sheffield. 2020. Search Engine Optimization and Business Communication Instruction: Interviews With Experts. Bus. Prof. Commun. Q. (January 2020), 232949061989033. https://doi.org/10.1177/2329490619890335Google Scholar
- Similarweb. 2021. SimilarWeb | Website Traffic Intelligence.Google Scholar
- Herbert Alexander Simon. 1955. A Behavioral Model of Rational Choice. Q. J. Econ. 69, 1 (1955), 99–118. https://doi.org/10.2307/1884852Google ScholarCross Ref
- StatCounter. 2020. Search Engine Market Share Europe | StatCounter Global Stats.Google Scholar
- Artur Strzelecki. 2020. Eye-Tracking Studies of Web Search Engines: A Systematic Literature Review. Information 11, 6 (June 2020). https://doi.org/10.3390/info11060300Google Scholar
- Ao-Jan Su, Y Charlie Hu, Aleksandar Kuzmanovic, and Cheng-Kok Koh. 2014. How to Improve Your Search Engine Ranking: Myths and Reality. Acm Trans. Web 8, 2 (2014), 8. https://doi.org/10.1145/2579990Google ScholarDigital Library
- Sebastian Sünkler and Nurce Yagci. 2021. Development and software implementation of a preliminary model to identify the probability of search engine optimization on webpages. Hamburg.Google Scholar
- Arthur Taylor and Heather A. Dalal. 2017. Gender and Information Literacy: Evaluation of Gender Differences in a Student Survey of Information Sources. Coll. Res. Libr. 78, 1 (2017), 90–113. https://doi.org/10.5860/crl.78.1.90Google ScholarCross Ref
- Shari Thurow. 2015. To Optimize Search, Optimize the Searcher. Online Search. 39, 4 (2015), 44–48.Google Scholar
- Shari Thurow and Nick Musica. 2009. When Search Meets Web Usability. New Riders, Berkeley.Google Scholar
- Andreas Tremel. 2010. Suchen, finden - glauben? Die Rolle der Glaubwürdigkeit von Suchergebnissen bei der Nutzung von Suchmaschinen. Ludwig-Maximilians-Universität (LMU) München.Google Scholar
- Lance Umenhofer. 2019. Gaining Ground: Search Engine Optimization and Its Implementation on an Indie Book Press. Publ. Res. Q. 35, 2 (June 2019), 258–273. https://doi.org/10.1007/s12109-019-09651-xGoogle Scholar
- Julian Unkel and Alexander Haas. 2017. The effects of credibility cues on the selection of search engine results. J. Assoc. Inf. Sci. Technol. 68, 8 (August 2017), 1850–1862. https://doi.org/10.1002/asi.23820Google ScholarDigital Library
- Eugene B. Visser and Melius Weideman. 2011. An empirical study on website usability elements and how they affect search engine optimisation. SA J. Inf. Manag. 13, 1 (March 2011), 1–9. https://doi.org/10.4102/sajim.v13i1.428Google Scholar
- Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016. Learning to rank with selection bias in personal search. SIGIR 2016 - Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. (2016), 115–124. https://doi.org/10.1145/2911451.2911537Google ScholarDigital Library
- Axel Westerwick. 2013. Effects of Sponsorship, Web Site Design, and Google Ranking on the Credibility of Online Information. J. Comput. Commun. 18, 2 (January 2013), 80–97. https://doi.org/10.1111/jcc4.12006Google ScholarDigital Library
- Yisong Yue, Rajan Patel, and Hein Roehrig. 2010. Beyond position bias. In Proceedings of the 19th international conference on World wide web - WWW ’10, ACM Press, New York, New York, USA, 1011. https://doi.org/10.1145/1772690.1772793Google ScholarDigital Library
- Hamed Zamani, Michael Bendersky, Xuanhui Wang, and Mingyang Zhang. 2017. Situational context for ranking in personal search. 26th Int. World Wide Web Conf. WWW 2017 (2017), 1531–1540. https://doi.org/10.1145/3038912.3052648Google ScholarDigital Library
- Lihong Zhang, Jianwei Zhang, and Yanbin Ju. 2011. The research on Search Engine Optimization based on Six Sigma Management. In 2011 International Conference on E-Business and E-Government (ICEE), IEEE, 1–4. https://doi.org/10.1109/ICEBEG.2011.5881880Google ScholarCross Ref
- Christos Ziakis, Maro Vlachopoulou, Theodosios Kyrkoudis, and Makrina Karagkiozidou. 2019. Important factors for improving Google search rank. Futur. Internet 11, 2 (2019). https://doi.org/10.3390/fi11020032Google Scholar
- Malte Ziewitz. 2019. Rethinking gaming: The ethical work of optimization in web search engines. Soc. Stud. Sci. 49, 5 (2019), 707–731. https://doi.org/10.1177/0306312719865607Google ScholarCross Ref
- George Kingsley Zipf. 1949. Human Behaviour and the Principle of Least Effort. https://doi.org/10.2307/2226729Google Scholar
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