skip to main content
10.1145/3295750.3298927acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article

Interplay of Documents' Readability, Comprehension and Consumer Health Search Performance Across Query Terminology

Published:08 March 2019Publication History

ABSTRACT

Because of terminology mismatches, health consumers frequently face difficulties while searching the Web for health information. Difficulties arise in query formulation but also in understanding the retrieved documents. In this work we analyze how documents' readability affects users' comprehension and how both affect the retrieval performance, measured in different ways. In addition, we analyze how performance measures relate with each other. For this purpose we have conducted a laboratory user study with 40 participants. We found that readability is essential for a document to be at least partially relevant and that it becomes even more important if the document has medico-scientific terminology. Moreover, the relevance of a document to a specific user highly depends on its comprehension. In lay queries we found the medical accuracy of users' answers is related to the session's relevance assessments. This shows that users can, at least in part, relate their relevance assessments with the medical accuracy of the documents. On the other hand, this relationship does not exist with medico-scientific queries.

References

  1. Maristella Agosti, Richard Berendsen, Toine Bogers, Martin Braschler, Paul Buitelaar, Khalid Choukri, Giorgio M. Di Nunzio, Nicola Ferro, Pamela Forner, Allan Hanbury, Karin F. Heppin, Preben Hansen, Anni Jävelin, Birger Larsen, Mihai Lupu, Ivano Masiero, Henning Müller, Simone Peruzzo, Vivien Petras, Florina Piroi, Maarten de Rijke, Giuseppe Santucci, Gianmaria Silvello, and Elaine Toms. 2012. PROMISE Retreat Report: Prospects and Opportunities for Information Access Evaluation. Technical Report. PROMISE network of excellence, grant agreement no. 258191.Google ScholarGoogle Scholar
  2. Jacob Cohen. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, Vol. 70, 4 (1968), 213--220. http://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0026256Google ScholarGoogle ScholarCross RefCross Ref
  3. Johanna Eerola and Pertti Vakkari. 2008. How a general and a specific thesaurus cover expressions in patients' questions and physicians' answers. Journal of Documentation, Vol. 64, 1 (2008), 131--142.Google ScholarGoogle ScholarCross RefCross Ref
  4. P. R. Fitzsimmons, B. D. Michael, J. L. Hulley, and G. O. Scott. 2010. A readability assessment of online Parkinson's disease information. The Journal of the Royal College of Physicians of Edinburgh, Vol. 40, 4 (Dec. 2010), 292--296.Google ScholarGoogle ScholarCross RefCross Ref
  5. Daniela B. Friedman, Laurie Hoffman-Goetz, and Jose F. Arocha. 2004. Readability of cancer information on the internet. Journal of Cancer Education, Vol. 19, 2 (2004), 117--122.Google ScholarGoogle ScholarCross RefCross Ref
  6. Daniela B. Friedman, Laurie Hoffman-Goetz, and Jose. F. Arocha. 2006. Health literacy and the World Wide Web: comparing the readability of leading incident cancers on the Internet. Medical informatics and the Internet in medicine, Vol. 31, 1 (March 2006), 67--87. http://view.ncbi.nlm.nih.gov/pubmed/16754369Google ScholarGoogle Scholar
  7. Ian C. Hoppe. 2010. Readability of patient information regarding breast cancer prevention from the Web site of the National Cancer Institute. Journal of Cancer Education, Vol. 25, 4 (Dec. 2010), 490--492. http://view.ncbi.nlm.nih.gov/pubmed/20238201Google ScholarGoogle ScholarCross RefCross Ref
  8. J. Richard Landis and Gary G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics, Vol. 33, 1 (1977), 159--174. https://www.jstor.org/stable/2529310Google ScholarGoogle ScholarCross RefCross Ref
  9. Gondy Leroy, James E. Endicott, David Kauchak, Obay Mouradi, and Melissa Just. 2013. User evaluation of the effects of a text simplification algorithm using term familiarity on perception, understanding, learning, and information retention. Journal of Medical Internet Research, Vol. 15, 7 (2013), e144.Google ScholarGoogle ScholarCross RefCross Ref
  10. Gondy Leroy, Stephen Helmreich, and James R. Cowie. 2010. The influence of text characteristics on perceived and actual difficulty of health information. International Journal of Medical Informatics, Vol. 79, 6 (June 2010), 438--449.Google ScholarGoogle ScholarCross RefCross Ref
  11. Gondy Leroy, David Kauchak, and Obay Mouradi. 2014. A user-study measuring the effects of lexical simplification and coherence enhancement on perceived and actual text difficulty. International Journal of Medical Informatics, Vol. 82, 8 (Aug. 2014), 717--730.Google ScholarGoogle Scholar
  12. Carla Teixeira Lopes and Cristina Ribeiro. 2013. Measuring the value of health query translation: An analysis by user language proficiency. Journal of the American Society for Information Science and Technology Vol. 64, 5 (May 2013), 951--963.Google ScholarGoogle ScholarCross RefCross Ref
  13. Carla Teixeira Lopes and Cristina Ribeiro. 2015. Effects of Terminology on Health Queries: An Analysis by User's Health Literacy and Topic Familiarity. Advances in Librarianship, Vol. 39 (2015), 145--184.Google ScholarGoogle ScholarCross RefCross Ref
  14. Carla Teixeira Lopes and Hugo Sousa. 2019. Assisting Health Consumers While Searching the Web Through Medical Annotations. In Proceedings of the 2019 Conference on Human Information Interaction & Retrieval. ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Wen-Hsiang H. Lu, Ray Shih-Jui, Yi-Che C. Chan, and Kuan-Hsi H. Chen. 2006. Overcoming terminology barrier using Web resources for cross-language medical information retrieval.. In AMIA Annual Symposium proceedings. American Medical Informatics Association (AMIA), Maryland, USA, 519--523. http://view.ncbi.nlm.nih.gov/pubmed/17238395Google ScholarGoogle Scholar
  16. Gang Luo. 2009. Design and Evaluation of the iMed Intelligent Medical Search Engine. In Proceedings of the 2009 IEEE International Conference on Data Engineering (ICDE'09). IEEE Computer Society, Washington, DC, USA, 1379--1390. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Gang Luo, Chunqiang Tang, Hao Yang, and Xing Wei. 2008. MedSearch: a specialized search engine for medical information retrieval. In CIKM'08: Proceedings of the 17th ACM Conference on Information and Knowledge Mining. ACM, New York, NY, USA, 143--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Gheorghe Muresan, Michael Cole, Catherine L. Smith, Lu Liu, and Nicholas J. Belkin. 2006. Does Familiarity Breed Content? Taking Account of Familiarity with a Topic in Personalizing Information Retrieval. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. HICSS'06., Vol. 3. IEEE, New York, NY, USA, 53c. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Joao Palotti, Lorraine Goeuriot, Guido Zuccon, and Allan Hanbury. 2016. Ranking Health Web Pages with Relevance and Understandability. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'16). ACM, New York, NY, USA, 965--968. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Stephen E. Robertson, Evangelos Kanoulas, and Emine Yilmaz. 2010. Extending average precision to graded relevance judgments. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10). ACM, New York, NY, USA, 603--610. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Tefko Saracevic. 1996. Relevance reconsidered. In Information science: Integration in perspectives. Proceedings of the Second Conference on Conceptions of Library and Information Science. Royal School of Librarianship, Copenhagen, 201--218.Google ScholarGoogle Scholar
  22. Chenhao Tan, Evgeniy Gabrilovich, and Bo Pang. 2012. To Each His Own: Personalized Content Selection Based on Text Comprehensibility. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM'12). ACM, New York, NY, USA, 233--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Kevyn C. Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, and David Sontag. 2011. Personalizing Web Search Results by Reading Level. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM'11). ACM, New York, NY, USA, 403--412. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Tiffany M. Walsh and Teresa A. Volsko. 2008. Readability assessment of internet-based consumer health information. Respiratory Care, Vol. 53, 10 (Oct. 2008), 1310--1315. http://view.ncbi.nlm.nih.gov/pubmed/18811992Google ScholarGoogle Scholar
  25. Q. Zeng, S. Kogan, N. Ash, R. A. Greenes, and A. A. Boxwala. 2002. Characteristics of consumer terminology for health information retrieval. Methods of Information in Medicine, Vol. 41, 4 (2002), 289--298. http://view.ncbi.nlm.nih.gov/pubmed/12425240Google ScholarGoogle ScholarCross RefCross Ref
  26. Qing T. Zeng, Jonathan Crowell, Robert M. Plovnick, Eunjung Kim, Long Ngo, and Emily Dibble. 2006. Assisting consumer health information retrieval with query recommendations. Journal of the American Medical Informatics Association, Vol. 13, 1 (2006), 80--90.Google ScholarGoogle ScholarCross RefCross Ref
  27. Guido Zuccon. 2016. Understandability Biased Evaluation for Information Retrieval. In Advances in Information Retrieval, Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio M.Di Nunzio, Claudia Hauff, and Gianmaria Silvello (Eds.). Lecture Notes in Computer Science, Vol. 9626. Springer International Publishing, Cham, 280--292.Google ScholarGoogle Scholar

Index Terms

  1. Interplay of Documents' Readability, Comprehension and Consumer Health Search Performance Across Query Terminology

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            CHIIR '19: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
            March 2019
            463 pages
            ISBN:9781450360258
            DOI:10.1145/3295750

            Copyright © 2019 ACM

            Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 8 March 2019

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate55of163submissions,34%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader