Do Topic Shift and Query Reformulation Patterns Correlate in Academic Search?

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: query reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important query reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and query reformulations. Surprisingly, users’ preferences of query reformulations correlate little with their topic shift tendency. However, certain reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization.

Notes

Acknowledgements

This research was supported by the China Scholarship Council and Elsevier. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.

References

  1. 1.
    Aiello, L.M., Donato, D., Ozertem, U., Menczer, F.: Behavior-driven clustering of queries into topics. In: CIKM, pp. 1373–1382. ACM (2011)Google Scholar
  2. 2.
    Beitzel, S.M., Jensen, E.C., Chowdhury, A., Frieder, O., Grossman, D.: Temporal analysis of a very large topically categorized web query log. J. Assoc. Inf. Sci. Technol. 58(2), 166–178 (2007)CrossRefGoogle Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  4. 4.
    Boldi, P., Bonchi, F., Castillo, C., Vigna, S.: Query reformulation mining: models, patterns, and applications. Inf. Retr. 14(3), 257–289 (2011)CrossRefGoogle Scholar
  5. 5.
    Bordino, I., Castillo, C., Donato, D., Gionis, A.: Query similarity by projecting the query-flow graph. In: SIGIR, pp. 515–522. ACM (2010)Google Scholar
  6. 6.
    Bruza, P.D., Dennis, S.: Query reformulation on the internet: empirical data and the hyperindex search engine. In: RIAO (1997)Google Scholar
  7. 7.
    Cai, F., de Rijke, M.: A survey of query auto completion in information retrieval. Found. Trends Inf. Retr. 10(4), 273–363 (2016)CrossRefGoogle Scholar
  8. 8.
    Catlow, J., Górny, M., Lewandowski, R.: Students as users of digital libraries. Qual. Quant. Methods Libr. 65, 60–61 (2015)Google Scholar
  9. 9.
    Chuklin, A., Markov, I., de Rijke, M.: Click Models for Web Search. Morgan & Claypool Publishers, San Rafael (2015)Google Scholar
  10. 10.
    Evans, J.S.B.T., Over, D.E.: Reasoning and Rationality. Psychology Press, Hove (1996)Google Scholar
  11. 11.
    Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: an automatic citation indexing system. In: DL, pp. 89–98. ACM (1998)Google Scholar
  12. 12.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  13. 13.
    Guan, D., Zhang, S., Yang, H.: Utilizing query change for session search. In: SIGIR, pp. 453–462. ACM (2013)Google Scholar
  14. 14.
    Han, H., Jeong, W., Wolfram, D.: Log analysis of academic digital library: user query patterns. In: iConference 2014 Proceedings, pp. 1002–1008 (2014)Google Scholar
  15. 15.
    Hemminger, B.M., Lu, D., Vaughan, K., Adams, S.J.: Information seeking behavior of academic scientists. J. Assoc. Inf. Sci. Technol. 58(14), 2205–2225 (2007)CrossRefGoogle Scholar
  16. 16.
    Hu, Y., Qian, Y., Li, H., Jiang, D., Pei, J., Zheng, Q.: Mining query subtopics from search log data. In: SIGIR, pp. 305–314. ACM (2012)Google Scholar
  17. 17.
    Hua, W., Song, Y., Wang, H., Zhou, X.: Identifying users’ topical tasks in web search. In: WSDM, pp. 93–102. ACM (2013)Google Scholar
  18. 18.
    Jansen, B.J., Booth, D.L., Spink, A.: Patterns of query reformulation during web searching. J. Assoc. Inf. Sci. Technol. 60(7), 1358–1371 (2009)CrossRefGoogle Scholar
  19. 19.
    Jeng, W., He, D., Jiang, J.: User participation in an academic social networking service: a survey of open group users on mendeley. J. Assoc. Inf. Sci. Technol. 66(5), 890–904 (2015)CrossRefGoogle Scholar
  20. 20.
    Jeng, W., Jiang, J., He, D.: Users’ perceived difficulties and corresponding reformulation strategies in Google voice search. J. Libr. Inf. Stud. 14(1), 25–39 (2016)Google Scholar
  21. 21.
    Jiang, J.-Y., Ke, Y.-Y., Chien, P.-Y., Cheng, P.-J.: Learning user reformulation behavior for query auto-completion. In: SIGIR, pp. 445–454. ACM (2014)Google Scholar
  22. 22.
    Jones, R., Klinkner, K.L.: Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In: CIKM, pp. 699–708. ACM (2008)Google Scholar
  23. 23.
    Jones, S., Cunningham, S.J., McNab, R., Boddie, S.: A transaction log analysis of a digital library. Int. J. Digit. Libr. 3(2), 152–169 (2000)CrossRefGoogle Scholar
  24. 24.
    Ke, H.-R., Kwakkelaar, R., Tai, Y.-M., Chen, L.-C.: Exploring behavior of e-journal users in science and technology: transaction log analysis of Elsevier’s ScienceDirect onsite in Taiwan. Libr. Inf. Sci. Res. 24(3), 265–291 (2002)CrossRefGoogle Scholar
  25. 25.
    Lau, T., Horvitz, E.: Patterns of search: analyzing and modeling web query refinement. In: Kay, J. (ed.) UM99 User Modeling. CICMS, vol. 407, pp. 119–128. Springer, Heidelberg (1999). doi: 10.1007/978-3-7091-2490-1_12 CrossRefGoogle Scholar
  26. 26.
    Li, L., Deng, H., He, Y., Dong, A., Chang, Y., Zha, H.: Behavior driven topic transition for search task identification. In: WWW, pp. 555–565. ACM (2016)Google Scholar
  27. 27.
    Li, X., Schijvenaars, R., de Rijke, M.: Investigating queries and search failures in academic search. Inf. Process. Manag. (2017, to appear)Google Scholar
  28. 28.
    Lindberg, D.: Internet access to the National Library of Medicine. Eff. Clin. Pract. 3(5), 256–260 (2000)Google Scholar
  29. 29.
    Mehrotra, R., Bhattacharya, P., Yilmaz, E.: Uncovering task based behavioral heterogeneities in online search behavior. In: SIGIR, pp. 1049–1052. ACM (2016)Google Scholar
  30. 30.
    Mohammad Arif, A.S., Du, J.T., Lee, I.: Examining collaborative query reformulation: a case of travel information searching. In: SIGIR, pp. 875–878. ACM (2014)Google Scholar
  31. 31.
    Niu, X., Hemminger, B.M., Lown, C., Adams, S., Brown, C., Level, A., McLure, M., Powers, A., Tennant, M.R., Cataldo, T.: National study of information seeking behavior of academic researchers in the United States. J. Assoc. Inf. Sci. Technol. 61(5), 869–890 (2010)CrossRefGoogle Scholar
  32. 32.
    Pontis, S., Blandford, A.: Understanding “influence:” an exploratory study of academics’ processes of knowledge construction through iterative and interactive information seeking. J. Assoc. Inf. Sci. Technol. 66(8), 1576–1593 (2015)CrossRefGoogle Scholar
  33. 33.
    Pontis, S., Blandford, A., Greifeneder, E., Attalla, H., Neal, D.: Keeping up to date: an academic researcher’s information journey. J. Assoc. Inf. Sci. Technol. 68(1), 22–35 (2017). Online since 22 October 2015CrossRefGoogle Scholar
  34. 34.
    Radlinski, F., Szummer, M., Craswell, N.: Inferring query intent from reformulations and clicks. In: WWW, pp. 1171–1172. ACM (2010)Google Scholar
  35. 35.
    Rieh, S.Y., et al.: Analysis of multiple query reformulations on the web: the interactive information retrieval context. Inf. Process. Manag. 42(3), 751–768 (2006)CrossRefGoogle Scholar
  36. 36.
    Shiri, A.: Query reformulation strategies in an interdisciplinary digital library: the case of nanoscience and technology. In: ICDIM, pp. 200–206. IEEE (2010)Google Scholar
  37. 37.
    Shokouhi, M., Jones, R., Ozertem, U., Raghunathan, K., Diaz, F.: Mobile query reformulations. In: SIGIR, pp. 1011–1014. ACM (2014)Google Scholar
  38. 38.
    Tang, J.: Aminer: toward understanding big scholar data. In: WSDM, p. 467. ACM (2016)Google Scholar
  39. 39.
    Teevan, J.: The re:search engine: simultaneous support for finding and re-finding. In: UIST, pp. 23–32. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands

Personalised recommendations