Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References CONCLUSION This study provides important insights into whether, although Google is by far the most popular search engine, the use of alternatives could benefit users. Our results show that using another or more than one search engine leads to seeing more diverse search results, allowing users to inform themselves more comprehensively. It should be noted that within each search engine's results, the concentration of sources shows that only a few top sources dominate the results, meaning whichever search engine a user chooses to use will shape what sources the information they get to see comes from. RESEARCH DATA Research data is available at: https://osf.io/nt3wv/ ACKNOWLEDGMENTS This work is funded by the German Research Foundation (DFG – Deutsche Forschungsgemeinschaft; Grant No. 460676551). REFERENCES Agrawal, R. (2016). Overlap in the Web Search Results of Google and Bing. Journal of Web Science, 2(1), 17–30. https://doi.org/10.1561/106.00000005 Bar-Ilan, J. (2005). Comparing rankings of search results on the Web. Information Processing & Management, 41(6), 1511–1519. https://doi.org/10.1016/J.IPM.2005.03.008 Bar‐Ilan, J., Levene, M., & Mat‐Hassan, M. (2006). Methods for evaluating dynamic changes in search engine rankings: a case study. Journal of Documentation, 62(6), 708–729. https://doi.org/10.1108/00220410610714930 Bharat, K. (1998). A technique for measuring the relative size and overlap of public Web search engines. Computer Networks and ISDN Systems, 30(1–7), 379–388. https://doi.org/10.1016/S0169-7552(98)00127-5 Bilal, D., & Ellis, R. (2011). Evaluating Leading Web Search Engines on Children’s Queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 6764 LNCS (Issue PART 4, pp. 549–558). https://doi.org/10.1007/978 3-642-21619- 0_67 Cardoso, B., & Magalhães, J. (2011). Google, bing and a new perspective on ranking similarity. Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM ’11, 1933–1936. https://doi.org/10.1145/2063576.2063858 Chignell, M. H., Gwizdka, J., & Bodner, R. C. (1999). Discriminating meta-search: a framework for evaluation. Information Processing & Management, 35(3), 337–362. https://doi.org/10.1016/S0306 4573(98)00065-X Ding, W., & Marchionini, G. (1996). A comparative study of web search service performance. Proceedings of the ASIS Annual Meeting, 33, 136–142. https://eric.ed.gov/?id=EJ557172 Edelman Trust Institute. (2022). Edelman Trust Barometer 2022 - Global Report. https://www.edelman.com/sites/g/files/aatuss191/files/2022-01/2022 Edelman Trust Barometer FINAL_Jan25.pdf European Commission. (2016). Special Eurobarometer 447 – Online Platforms. European Commission. https://doi.org/10.2759/937517 Gini, C. (1936). On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series, 208(1), 73–79. Goel, S., Broder, A., Gabrilovich, E., & Pang, B. (2010). Anatomy of the long tail. Proceedings of the Third ACM International Conference on Web Search and Data Mining - WSDM ’10, 201. https://doi.org/10.1145/1718487.1718513 González, C. G., Bonventi, W., & Rodrigues, A. L. V. (2008). Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 5249 LNAI (pp. 8–22). Springer Verlag. https://doi.org/10.1007/978-3-540-88190-2_7 Höchstötter, N., & Lewandowski, D. (2009). What users see – Structures in search engine results pages. Information Sciences, 179(12), 1796–1812. https://doi.org/10.1016/j.ins.2009.01.028 Introna, L. D., & Nissenbaum, H. (2000). Shaping the Web: Why the Politics of Search Engines Matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634 Lawrence, S., & Giles, C. L. (1999). Accessibility of information on the web. Nature, 400(6740), 107 107. https://doi.org/10.1038/21987 Lewandowski, D. (2019). The web is missing an essential part of infrastructure. Communications of the ACM, 62(4) 24–24. https://doi.org/10.1145/3312479 Lewandowski, D., & Kammerer, Y. (2021). Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behaviour & Information Technology, 40(14), 1485–1515. https://doi.org/10.1080/0144929X.2020.1761450 Lewandowski, D., & Sünkler, S. (2019). What does Google recommend when you want to compare insurance offerings? Aslib Journal of Information Management, 71(3), 310–324. https://doi.org/10.1108/AJIM-07-2018- 0172 Mager, A. (2014). Is Small Really Beautiful? Big Search and Its Alternatives. In R. König & M. Rasch (Eds.), Society of the Query Reader (pp. 59–72). Institute of Network Cultures. Makhortykh, M., Urman, A., & Ulloa, R. (2020). How search engines disseminate information about COVID-19 and why they should do better. Harvard Kennedy School Misinformation Review, 1(May), 1–12. https://doi.org/10.37016/mr-2020-017 Meng, W., Yu, C., & Liu, K.-L. (2002). Building efficient and effective metasearch engines. ACM Computing Surveys, 34(1), 48–89. https://doi.org/10.1145/505282.505284 Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C., & Kleis Nielsen, R. (2021). The Reuters Institute Digital News Report 2021. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2021 06/Digital_News_Report_2021_FINAL.pdf Ortega, F., Gonzalez-Barahona, J. M., & Robles, G. (2008). On the Inequality of Contributions to Wikipedia. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 304–304. https://doi.org/10.1109/HICSS.2008.333 Purcell, K., Brenner, J., & Rainie, L. (2012). Search Engine Use 2012. https://www.pewresearch.org/internet/wpcontent/uploads/sites/9/media/Files/Reports/2012/PIP_ earch_Engine_Use_2012.pdf Puschmann, C. (2019). Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626 Schultheiß, S., & Lewandowski, D. (2021). A representative online survey among German search engine users with a focus on questions regarding search engine optimization (SEO): a study within the SEO Effect project - Working Paper 2. https://osf.io/wzhxs Spink, A., Jansen, B. J., Blakely, C., & Koshman, S. (2006). A study of results overlap and uniqueness among major Web search engines. Information Processing and Management, 42(5), 1379–1391. https://doi.org/10.1016/j.ipm.2005.11.001 StatCounter. (2022). Search Engine Market Share. https://gs.statcounter.com/search-engine market-share/ Steiner, M., Magin, M., Stark, B., & Geiß, S. (2022). Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries. Information, Communication & Society, 25(2), 217–241. https://doi.org/10.1080/1369118X.2020.1776367 Sünkler, S., Lewandowski, D., Schultheiß, S., Yagci, N., Sygulla, D., & von Mach, S. (2022). Relevance Assessment Tool. osf.io/t3hg9 Thelwall, M. (2008). Quantitative comparisons of search engine results. Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. https://doi.org/10.1002/asi.20834 Unkel, J., & Haim, M. (2021). Googling Politics: Parties, Sources, and Issue Ownerships on Google in the 2017 German Federal Election Campaign. Social Science Computer Review, 39(5), 844–861. https://doi.org/10.1177/0894439319881634 This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References CONCLUSION This study provides important insights into whether, although Google is by far the most popular search engine, the use of alternatives could benefit users. Our results show that using another or more than one search engine leads to seeing more diverse search results, allowing users to inform themselves more comprehensively. It should be noted that within each search engine's results, the concentration of sources shows that only a few top sources dominate the results, meaning whichever search engine a user chooses to use will shape what sources the information they get to see comes from. RESEARCH DATA Research data is available at: https://osf.io/nt3wv/ ACKNOWLEDGMENTS This work is funded by the German Research Foundation (DFG – Deutsche Forschungsgemeinschaft; Grant No. 460676551). REFERENCES Agrawal, R. (2016). Overlap in the Web Search Results of Google and Bing. Journal of Web Science, 2(1), 17–30. https://doi.org/10.1561/106.00000005 Bar-Ilan, J. (2005). Comparing rankings of search results on the Web. Information Processing & Management, 41(6), 1511–1519. https://doi.org/10.1016/J.IPM.2005.03.008 Bar‐Ilan, J., Levene, M., & Mat‐Hassan, M. (2006). Methods for evaluating dynamic changes in search engine rankings: a case study. Journal of Documentation, 62(6), 708–729. https://doi.org/10.1108/00220410610714930 Bharat, K. (1998). A technique for measuring the relative size and overlap of public Web search engines. Computer Networks and ISDN Systems, 30(1–7), 379–388. https://doi.org/10.1016/S0169-7552(98)00127-5 Bilal, D., & Ellis, R. (2011). Evaluating Leading Web Search Engines on Children’s Queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 6764 LNCS (Issue PART 4, pp. 549–558). https://doi.org/10.1007/978 3-642-21619- 0_67 Cardoso, B., & Magalhães, J. (2011). Google, bing and a new perspective on ranking similarity. Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM ’11, 1933–1936. https://doi.org/10.1145/2063576.2063858 Chignell, M. H., Gwizdka, J., & Bodner, R. C. (1999). Discriminating meta-search: a framework for evaluation. Information Processing & Management, 35(3), 337–362. https://doi.org/10.1016/S0306 4573(98)00065-X Ding, W., & Marchionini, G. (1996). A comparative study of web search service performance. Proceedings of the ASIS Annual Meeting, 33, 136–142. https://eric.ed.gov/?id=EJ557172 Edelman Trust Institute. (2022). Edelman Trust Barometer 2022 - Global Report. https://www.edelman.com/sites/g/files/aatuss191/files/2022-01/2022 Edelman Trust Barometer FINAL_Jan25.pdf European Commission. (2016). Special Eurobarometer 447 – Online Platforms. European Commission. https://doi.org/10.2759/937517 Gini, C. (1936). On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series, 208(1), 73–79. Goel, S., Broder, A., Gabrilovich, E., & Pang, B. (2010). Anatomy of the long tail. Proceedings of the Third ACM International Conference on Web Search and Data Mining - WSDM ’10, 201. https://doi.org/10.1145/1718487.1718513 González, C. G., Bonventi, W., & Rodrigues, A. L. V. (2008). Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 5249 LNAI (pp. 8–22). Springer Verlag. https://doi.org/10.1007/978-3-540-88190-2_7 Höchstötter, N., & Lewandowski, D. (2009). What users see – Structures in search engine results pages. Information Sciences, 179(12), 1796–1812. https://doi.org/10.1016/j.ins.2009.01.028 Introna, L. D., & Nissenbaum, H. (2000). Shaping the Web: Why the Politics of Search Engines Matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634 Lawrence, S., & Giles, C. L. (1999). Accessibility of information on the web. Nature, 400(6740), 107 107. https://doi.org/10.1038/21987 Lewandowski, D. (2019). The web is missing an essential part of infrastructure. Communications of the ACM, 62(4) 24–24. https://doi.org/10.1145/3312479 Lewandowski, D., & Kammerer, Y. (2021). Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behaviour & Information Technology, 40(14), 1485–1515. https://doi.org/10.1080/0144929X.2020.1761450 Lewandowski, D., & Sünkler, S. (2019). What does Google recommend when you want to compare insurance offerings? Aslib Journal of Information Management, 71(3), 310–324. https://doi.org/10.1108/AJIM-07-2018- 0172 Mager, A. (2014). Is Small Really Beautiful? Big Search and Its Alternatives. In R. König & M. Rasch (Eds.), Society of the Query Reader (pp. 59–72). Institute of Network Cultures. Makhortykh, M., Urman, A., & Ulloa, R. (2020). How search engines disseminate information about COVID-19 and why they should do better. Harvard Kennedy School Misinformation Review, 1(May), 1–12. https://doi.org/10.37016/mr-2020-017 Meng, W., Yu, C., & Liu, K.-L. (2002). Building efficient and effective metasearch engines. ACM Computing Surveys, 34(1), 48–89. https://doi.org/10.1145/505282.505284 Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C., & Kleis Nielsen, R. (2021). The Reuters Institute Digital News Report 2021. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2021 06/Digital_News_Report_2021_FINAL.pdf Ortega, F., Gonzalez-Barahona, J. M., & Robles, G. (2008). On the Inequality of Contributions to Wikipedia. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 304–304. https://doi.org/10.1109/HICSS.2008.333 Purcell, K., Brenner, J., & Rainie, L. (2012). Search Engine Use 2012. https://www.pewresearch.org/internet/wpcontent/uploads/sites/9/media/Files/Reports/2012/PIP_ earch_Engine_Use_2012.pdf Puschmann, C. (2019). Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626 Schultheiß, S., & Lewandowski, D. (2021). A representative online survey among German search engine users with a focus on questions regarding search engine optimization (SEO): a study within the SEO Effect project - Working Paper 2. https://osf.io/wzhxs Spink, A., Jansen, B. J., Blakely, C., & Koshman, S. (2006). A study of results overlap and uniqueness among major Web search engines. Information Processing and Management, 42(5), 1379–1391. https://doi.org/10.1016/j.ipm.2005.11.001 StatCounter. (2022). Search Engine Market Share. https://gs.statcounter.com/search-engine market-share/ Steiner, M., Magin, M., Stark, B., & Geiß, S. (2022). Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries. Information, Communication & Society, 25(2), 217–241. https://doi.org/10.1080/1369118X.2020.1776367 Sünkler, S., Lewandowski, D., Schultheiß, S., Yagci, N., Sygulla, D., & von Mach, S. (2022). Relevance Assessment Tool. osf.io/t3hg9 Thelwall, M. (2008). Quantitative comparisons of search engine results. Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. https://doi.org/10.1002/asi.20834 Unkel, J., & Haim, M. (2021). Googling Politics: Parties, Sources, and Issue Ownerships on Google in the 2017 German Federal Election Campaign. Social Science Computer Review, 39(5), 844–861. https://doi.org/10.1177/0894439319881634 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Differences in Top Search Results Between Google and Alternative Search Engines

Read My Stories

Too Long; Didn't Read

Make resilience your competitive advantage

Exploring Benefits of Using Alternative Search Engines

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Differences in Top Search Results Between Google and Alternative Search Engines

Examining Source Diversity in Web Search: A Comparative Study of Top Search Engines

How Different Search Engines Display and Prioritize Information

Comparative Analysis of Search Engine Results Using Google Trends Data

Differences in Top Search Results Between Google and Alternative Search Engines

Differences in Top Search Results Between Google and Alternative Search Engines

Examining Source Diversity in Web Search: A Comparative Study of Top Search Engines

How Different Search Engines Display and Prioritize Information

Comparative Analysis of Search Engine Results Using Google Trends Data

Differences in Top Search Results Between Google and Alternative Search Engines

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps