Skip to main content

Investigation of Bias in Web Search Queries

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13982))

Included in the following conference series:

  • 1522 Accesses

Abstract

The dissertation investigates the correlations and effects between biases in search queries and search query suggestions, search results, and users’ states of knowledge. Search engines are an important factor in opinion formation, while search queries determine the information a user is exposed to in information search. Search query suggestions play a crucial role in what users search for [22]. Biased query suggestions can be especially problematic if a user’s information need is not set and the interaction with query suggestions is likely. Only recently, research has started to investigate the general assumption that biased search queries lead to biased search results, focusing on political stance bias [17]. However, the correlation between biases in search queries and biases in search results has not been sufficiently investigated. Sparse context and limited data access pose challenges in detecting biases in search queries. This dissertation thus contributes datasets and methodological approaches that enable media bias research in the field of search queries and search query suggestions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baker, P., Potts, A.: ‘Why do white people have thin lips?’ Google and the perpetuation of stereotypes via auto-complete search forms, vol. 10, pp. 187–204. Routledge (2013). https://doi.org/10.1080/17405904.2012.744320

  2. Balog, K.: Entity-Oriented Search, The Information Retrieval Series, vol. 39. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93935-3

  3. Blackledge, C., Atapour-Abarghouei, A.: Transforming fake news: robust generalisable news classification using transformers. arXiv Version Number: 2 (2021). https://doi.org/10.48550/ARXIV.2109.09796

  4. Bolukbasi, T., Chang, K.W., Zou, J., Saligrama, V., Kalai, A.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 4356–4364. Curran Associates Inc., Red Hook, NY, USA (2016)

    Google Scholar 

  5. Bonart, M., Samokhina, A., Heisenberg, G., Schaer, P.: An investigation of biases in web search engine query suggestions. Online Inf. Rev. 44(2), 365–381 (2019). https://doi.org/10.1108/oir-11-2018-0341

    Article  Google Scholar 

  6. Cai, F., de Rijke, M.: A Survey of Query Auto Completion in Information Retrieval, vol. 10, pp. 273–363 (2016). https://doi.org/10.1561/1500000055

  7. Dallmann, A., Lemmerich, F., Zoller, D., Hotho, A.: Media bias in German online newspapers. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT 2015, pp. 133–137. Association for Computing Machinery, New York, NY, USA (2015). https://doi.org/10.1145/2700171.2791057

  8. Dean, B.: We Analyzed 5 Million Google Search Results. Here’s What We Learned About Organic CTR, August 2019. https://backlinko.com/google-ctr-stats

  9. Edelman: 2022 edelman trust barometer (2022). https://www.edelman.com/trust/2022-trust-barometer

  10. Epstein, R., Robertson, R.E.: The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections, vol. 112, pp. E4512–E4521. National Academy of Sciences Section: PNAS Plus, August 2015. https://doi.org/10.1073/pnas.1419828112

  11. Feuer, A., Savev, S., Aslam, J.A.: Evaluation of phrasal query suggestions. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 841–848. Association for Computing Machinery, New York, NY, USA (2007). https://doi.org/10.1145/1321440.1321556

  12. Haak, F., Schaer, P.: Perception-aware bias detection for query suggestions. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds.) BIAS 2021. CCIS, vol. 1418, pp. 130–142. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78818-6_12

    Chapter  Google Scholar 

  13. Haak, F., Schaer, P.: Auditing search query suggestion bias through recursive algorithm interrogation. In: WebSci 2022: 14th ACM Web Science Conference 2022, Barcelona, Spain, 26–29 June 2022, pp. 219–227. ACM (2022). https://doi.org/10.1145/3501247.3531567

  14. Hamborg, F., Donnay, K., Gipp, B.: Automated identification of media bias in news articles: an interdisciplinary literature review. Int. J. Digit. Libr. 20(4), 391–415 (2018). https://doi.org/10.1007/s00799-018-0261-y

    Article  Google Scholar 

  15. Hofmann, K., Mitra, B., Radlinski, F., Shokouhi, M.: An eye-tracking study of user interactions with query auto completion. In: Li, J., Wang, X.S., Garofalakis, M.N., Soboroff, I., Suel, T., Wang, M. (eds.) Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 3–7 November 2014, pp. 549–558. ACM (2014). https://doi.org/10.1145/2661829.2661922

  16. Introna, L., Nissenbaum, H.: Defining the web: the politics of search engines, vol. 33, pp. 54–62. Institute of Electrical and Electronics Engineers (IEEE) (2000). https://doi.org/10.1109/2.816269

  17. Kulshrestha, J., et al.: Search bias quantification: investigating political bias in social media and web search. Inf. Retrieval J. 22(4), 188–227 (2018). https://doi.org/10.1007/s10791-018-9341-2

  18. Jeong Lim, S., Jatowt, A., Yoshikawa, M.: DEIM forum 2018 C 1–3 towards bias inducing word detection by linguistic cue analysis in news articles (2018)

    Google Scholar 

  19. Jeong Lim, S., Jatowt, A., Yoshikawa, M.: Understanding characteristics of biased sentences in news articles. In: CIKM Workshops (2018)

    Google Scholar 

  20. Mertens, A., Pradel, F., Rozyjumayeva, A., Wäckerle, J.: As the tweet, so the reply? In: Proceedings of the 10th ACM Conference on Web Science - WebSci 2019. ACM Press (2019). https://doi.org/10.1145/3292522.3326013

  21. Mitra, B., Shokouhi, M., Radlinski, F., Hofmann, K.: On user interactions with query auto-completion. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM (2014). https://doi.org/10.1145/2600428.2609508

  22. Niu, X., Kelly, D.: The use of query suggestions during information search. Inf. Process. Manage. 50, 218–234 (2014). https://doi.org/10.1016/j.ipm.2013.09.002

    Article  Google Scholar 

  23. Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Proceedings of the 1st International Conference on Scalable Information Systems, p. 1-es. InfoScale 2006. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1146847.1146848

  24. Pitoura, E., et al.: On measuring bias in online information. In: CoRR. vol. abs/1704.05730 (2017)

    Google Scholar 

  25. Pradel, F.: Biased representation of politicians in Google and Wikipedia search? Joint Effect Party Identity Gender Identity Elections 38, 447–478 (2021). https://doi.org/10.1080/10584609.2020.1793846

    Article  Google Scholar 

  26. Ray, L.: 2020 Google search survey: how much do users trust their search results? (2020). https://moz.com/blog/2020-google-search-survey

  27. Robertson, R.E., Jiang, S., Lazer, D., Wilson, C.: Auditing autocomplete: suggestion networks and recursive algorithm interrogation. In: Proceedings of the 10th ACM Conference on Web Science, WebSci 2019, pp. 235–244. ACM, New York, NY, USA (2019). https://doi.org/10.1145/3292522.3326047

  28. Robertson, R.E., Lazer, D., Wilson, C.: Auditing the personalization and composition of politically-related search engine results pages. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 955–965. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3178876.3186143

  29. Spinde, T., Jeggle, C., Haupt, M., Gaissmaier, W., Giese, H.: How do we raise media bias awareness effectively? Effects of visualizations to communicate bias. PLOS ONE 17(4), 1–14 (2022). https://doi.org/10.1371/journal.pone.0266204

  30. Spinde, T., Plank, M., Krieger, J.D., Ruas, T., Gipp, B., Aizawa, A.: Neural media bias detection using distant supervision with BABE - bias annotations by experts. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 1166–1177. Association for Computational Linguistics, Punta Cana, Dominican Republic, November 2021. https://doi.org/10.18653/v1/2021.findings-emnlp.101

  31. Stier, S., et al.: Systematically monitoring social media: the case of the German federal election 2017 (2018). https://doi.org/10.17605/OSF.IO/5ZPM9

  32. Ter, A., Proper, H., Weide, T.: Query formulation as an information retrieval problem. Comput. J. 39, 255–274 (1996). https://doi.org/10.1093/comjnl/39.4.255

  33. Wang, P., et al.: Game of missuggestions: semantic analysis of search-autocomplete manipulations, January 2018. https://doi.org/10.14722/ndss.2018.23071

  34. Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: SPARK: adapting keyword query to semantic search. In: Aberer, K., et al. (eds.) The Semantic Web, pp. 694–707. Springer, Cham (2007). https://doi.org/10.1007/978-3-540-76298-0_50

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabian Haak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Haak, F. (2023). Investigation of Bias in Web Search Queries. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28241-6_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28240-9

  • Online ISBN: 978-3-031-28241-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics