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On Retraction Cascade? Citation Intention Analysis as a Quality Control Mechanism in Digital Libraries

  • Conference paper
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Linking Theory and Practice of Digital Libraries (TPDL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14241))

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Abstract

The amount of information in digital libraries (DLs) has been experiencing rapid growth. With the intense competition for research breakthroughs, researchers often intentionally or unintentionally fail to adhere to scientific standards, leading to the retraction of scientific articles. When a paper gets retracted, all its citing articles have to be verified to ensure the overall correctness of the information in digital libraries. Since this subjective verification is extremely time and resource-consuming, we propose a triage process that focuses on papers that imply a dependence on retracted articles, thus requiring further reevaluation. This paper seeks to establish a systematic approach for identifying and scrutinizing scholarly works that draw upon retracted work by direct citations, thus emphasizing the importance of further evaluation within the scholarly discourse. Firstly, we categorized and identified the intention in the citation context using verbs with predicative complements and cue phrases. Secondly, we classified the citation intentions of the retracted articles into dependent (if the citing paper is based on or incorporates part of the cited retracted work) and non-dependent (if the citing article discusses, criticizes, or negates the cited work). Finally, we compared the existing state-of-the-art literature and found that our proposed triage process can aid in ensuring the integrity of scientific literature, thereby enhancing its quality.

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Notes

  1. 1.

    While the FAIR principles were originally designed for scientific data management and stewardship, their adaptation to scientific publications is quite straightforward, see FAIR Principles - GO FAIR (go-fair.org).

  2. 2.

    Retraction Watch Database (retractiondatabase.org).

  3. 3.

    Retraction Watch Database (retractiondatabase.org).

  4. 4.

    https://github.com/Conferences2023/TPDL.

  5. 5.

    Retraction Watch Database (retractiondatabase.org).

  6. 6.

    Scite: see how research has been cited.

References

  1. Resnik, D.B.: From baltimore to bell labs: reflections on two decades of debate about scientific misconduct. Account Res. 10, 123–135 (2003). https://doi.org/10.1080/08989620300508

    Article  Google Scholar 

  2. Bar-Ilan, J., Halevi, G.: Temporal characteristics of retracted articles. Scientometrics 116(3), 1771–1783 (2018). https://doi.org/10.1007/s11192-018-2802-y

    Article  Google Scholar 

  3. Hernández-Alvarez, M., Gomez, J.M.: Survey about citation context analysis: tasks, techniques, and resources. Nat. Lang. Eng. 22(3), 327–349 (2016). https://doi.org/10.1017/S1351324915000388

    Article  Google Scholar 

  4. Aljohani, N.R., Fayoumi, A., Hassan, S.U.: An in-text citation classification predictive model for a scholarly search system. Scientometrics 126(7), 5509–5529 (2021). https://doi.org/10.1007/s11192-021-03986-z

    Article  Google Scholar 

  5. The Editors of The Lancet Retraction—Ileal-Lymphoid-Nodular Hyperplasia, Non-Specific Colitis, and Pervasive Developmental Disorder in Children. Lancet 375, 445 (2010). https://doi.org/10.1016/S0140-6736(10)60175-4

  6. Cohan, A., Ammar, W., van Zuylen, M., Cady, F.: Structural scaffolds for citation intent classification in scientific publications. In: Proceedings of NAACL-HLT, pp. 3586–3596 (2019). https://doi.org/10.18653/v1/N19-1361

  7. Bakhti, K., Niu, Z., Yousif, A., Nyamawe, A.S.: Citation function classification based on ontologies and convolutional neural networks. In: Learning Technology for Education Challenges: 7th International Workshop, LTEC 2018, Žilina, Slovakia, August 6–10, 2018, Proceedings 7, pp. 105–115. Springer International Publishing (2018). https://doi.org/10.1007/978-3-319-95522-3_10

  8. Ihsan, I., Qadir, M.A.: CCRO: citation’s context & reasons ontology. IEEE Access 7, 30423–30436 (2019). https://doi.org/10.1109/ACCESS.2019.2903450

    Article  Google Scholar 

  9. Jiang, X., Cai, C., Fan, W., Liu, T., Chen, J.: Contextualised modelling for effective citation function classification. In: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval, pp. 93-103 (2022). https://doi.org/10.1145/3582768.3582769

  10. Te, S., Barhoumi, A., Lentschat, M., Bordignon, F., Labbé, C., Portet, F.: Citation context classification: critical vs. non-critical. In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 49–53 (2022)

    Google Scholar 

  11. Valenzuela, M., Ha, V., Etzioni, O.: Identifying meaningful citations. In: AAAI Workshop: Scholarly Big Data, vol. 15, p. 13 (2015)

    Google Scholar 

  12. Wang, M., Zhang, J., Jiao, S., Zhang, X., Zhu, N., Chen, G.: Important citation identification by exploiting the syntactic and contextual information of citations. Scientometrics 125, 2109–2129 (2020). https://doi.org/10.1007/s11192-020-03677-1

    Article  Google Scholar 

  13. Zhu, X., Turney, P., Lemire, D., Vellino, A.: Measuring academic influence: not all citations are equal. J. Assoc. Inf. Sci. Technol. 66, 408–427 (2015). https://doi.org/10.1002/asi.23179

    Article  Google Scholar 

  14. Peroni, S., Shotton, D.: FaBiO and CiTO: ontologies for describing bibliographic resources and citations. J. Web Semant. 17, 33–43 (2012). https://doi.org/10.1016/j.websem.2012.08.001

    Article  Google Scholar 

  15. Mayernik, M.S., Phillips, J., Nienhouse, E.: Linking publications and data: challenges, trends, and opportunities. D-Lib Mag. 22(5/6), 11 (2016). https://doi.org/10.1045/may2016-mayernik

    Article  Google Scholar 

  16. Ciancarini, P., Iorio, A.D., Nuzzolese, A.G., Peroni, S., Vitali, F.: Evaluating citation functions in CiTO: cognitive issues. In: European Semantic Web Conference, pp. 580–594. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_39

  17. Willighagen, E.: Two years of explicit CiTO annotations. J. Cheminformatics 15(1), 14 (2023). https://doi.org/10.1186/s13321-023-00683-2

    Article  Google Scholar 

  18. Hsiao, T.-K., Schneider, J.: Continued use of retracted papers: temporal trends in citations and (lack of) awareness of retractions shown in citation contexts in biomedicine. Quant. Sci. Stud. 2, 1144–1169 (2021). https://doi.org/10.1162/qss_a_00155

    Article  Google Scholar 

  19. Bar-Ilan, J., Halevi, G.: Post retraction citations in context: a case study—Scientometrics 113(1), 547–565 (2017). https://doi.org/10.1007/s11192-017-2242-0

  20. Feng, L., Yuan, J., Yang, L.: An observation framework for retracted publications in multiple dimensions. Scientometrics 125, 1445–1457 (2020). https://doi.org/10.1007/s11192-020-03702-3

    Article  Google Scholar 

  21. Wakefield, A., et al.: RETRACTED: ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. Lancet 351, 637–641 (1998). https://doi.org/10.1016/S0140-6736(97)11096-0

    Article  Google Scholar 

  22. Heibi, I., Peroni, S.: A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.’s case. Scientometrics 126(10), 8433–8470 (2021). https://doi.org/10.1007/s11192-021-04097-5

  23. Heibi, I., Peroni, S.: A quantitative and qualitative open citation analysis of retracted articles in the humanities Quant. Sci. Stud. 1–46 (2022).https://doi.org/10.1162/qss_a_00222

  24. Fu, Y., Schneider, J.: Towards knowledge maintenance in scientific digital libraries with the keystone framework. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020. ACM: New York, NY, USA, August 2020; pp. 217–226 (2020). https://doi.org/10.1145/3383583.3398514

  25. Addepalli, A., Subin, K.A., Schneider, J.: Testing the keystone framework by analyzing positive citations to Wakefield’s 1998 paper. In: International Conference on Information, pp. 79–88. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96957-8_9

  26. Schneider, J., Ye, D., Hill, A.M., Whitehorn, A.S.: Continued post-retraction citation of a fraudulent clinical trial report, 11 years after it was retracted for falsifying data. Scientometrics 125, 2877–2913 (2020). https://doi.org/10.1007/s11192-020-03631-1

    Article  Google Scholar 

  27. Hsiao, T.K., Schneider, J.: Continued use of retracted papers: temporal trends in citations and (lack of) awareness of retractions shown in citation contexts in biomedicine. Quant. Sci. Stud. 2(4), 1144–1169 (2021). https://doi.org/10.1162/qss_a_00155

    Article  Google Scholar 

  28. Williams, P., Wager, E.: Exploring why and how journal editors retract articles: findings from a qualitative study. Sci. Eng. Ethics 19(1), 1–11 (2013). https://doi.org/10.1007/s11948-011-9292-0

    Article  Google Scholar 

  29. Levin, B.: English verb classes and alternations: a preliminary investigation. University of Chicago Press (1993)

    Google Scholar 

  30. Kipper, K., Korhonen, A., Ryant, N., Palmer, M.: A large-scale classification of English verbs. Lang. Resour. Eval. 42, 21–40 (2008). https://doi.org/10.1007/s10579-007-9048-2

    Article  Google Scholar 

  31. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306

    Article  MathSciNet  MATH  Google Scholar 

  32. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). https://doi.org/10.48550/arXiv.1810.04805

  33. Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2. IEEE (2018). https://doi.org/10.1109/IWQoS.2018.8624183

  34. Ezen-Can, A.: A comparison of LSTM and BERT for small corpus. arXiv preprint arXiv:2009.05451 (2020). https://doi.org/10.48550/arXiv.2009.05451

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Acknowledgments

Supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): PubPharm – the Specialized Information Service for Pharmacy (Gepris 267140244).

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Authors and Affiliations

  1. Institute for Information Systems, TU Braunschweig, Braunschweig, Germany

    Muhammad Usman & Wolf-Tilo Balke

Authors
  1. Muhammad Usman
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  2. Wolf-Tilo Balke
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Corresponding author

Correspondence to Muhammad Usman .

Editor information

Editors and Affiliations

  1. Amazon, Santa Clara, CA, USA

    Omar Alonso

  2. DataCite, Hannover, Germany

    Helena Cousijn

  3. University of Padua, Padua, Italy

    Gianmaria Silvello

  4. Europeana Foundation, BE Den Haag, The Netherlands

    Mónica Marrero

  5. University of Porto, Porto, Portugal

    Carla Teixeira Lopes

  6. University of Padua, Padua, Italy

    Stefano Marchesin

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Usman, M., Balke, WT. (2023). On Retraction Cascade? Citation Intention Analysis as a Quality Control Mechanism in Digital Libraries. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-43849-3_11

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  • Online ISBN: 978-3-031-43849-3

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