Skip to main content
SpringerLink
Log in

The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

Included in the following conference series:

Abstract

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Cross-Language Evaluation Forum (CLEF). The lab evaluates technology supporting various tasks related to factuality, and it is offered in Arabic, Bulgarian, English, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking (focusing on COVID-19). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to predict the veracity of a target news article and its topical domain. The evaluation is carried out using mean average precision or precision at rank k for the ranking tasks, and F\(_1\) for the classification tasks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
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. Agirre, E., et al.: SemEval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval 2016, pp. 497–511 (2016)

    Google Scholar 

  2. Alam, F., et al.: Fighting the COVID-19 infodemic in social media: a holistic perspective and a call to arms. ArXiv preprint 2007.07996 (2020)

    Google Scholar 

  3. Alam, F., et al.: Fighting the COVID-19 infodemic: modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society. ArXiv preprint 2005.00033 (2020)

    Google Scholar 

  4. Atanasova, P., et al.: Overview of the CLEF-2018 CheckThat! lab on automatic identification and verification of political claims. Task 1: Check-worthiness. In: Cappellato, L., Ferro, N., Nie, J.Y., Soulier, L. (eds.) Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2018)

    Google Scholar 

  5. Atanasova, P., Nakov, P., Karadzhov, G., Mohtarami, M., Da San Martino, G.: Overview of the CLEF-2019 CheckThat! lab on automatic identification and verification of claims. Task 1: Check-worthiness. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2019)

    Google Scholar 

  6. Ba, M.L., Berti-Equille, L., Shah, K., Hammady, H.M.: VERA: a platform for veracity estimation over web data. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, pp. 159–162 (2016)

    Google Scholar 

  7. Baly, R., et al.: What was written vs. who read it: news media profiling using text analysis and social media context. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 3364–3374 (2020)

    Google Scholar 

  8. Barrón-Cedeño, A., et al.: Overview of CheckThat! 2020 - automatic identification and verification of claims in social media. In: Proceedings of the 11th International Conference of the CLEF Association: Experimental IR Meets Multilinguality, Multimodality, and Interaction, CLEF 2020, pp. 215–236 (2020)

    Google Scholar 

  9. Barrón-Cedeño, A., et al.: Overview of the CLEF-2018 CheckThat! lab on automatic identification and verification of political claims. Task 2: Factuality. In: Cappellato, L., Ferro, N., Nie, J.Y., Soulier, L. (eds.) Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2018)

    Google Scholar 

  10. Bouziane, M., Perrin, H., Cluzeau, A., Mardas, J., Sadeq, A.: Buster.AI at CheckThat! 2020: Insights and recommendations to improve fact-checking. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  11. Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.): CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  12. Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.): Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2019)

    Google Scholar 

  13. Cappellato, L., Ferro, N., Nie, J.Y., Soulier, L. (eds.): Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2018)

    Google Scholar 

  14. Cazalens, S., Lamarre, P., Leblay, J., Manolescu, I., Tannier, X.: A content management perspective on fact-checking. Proceedings of the International Conference on World Wide Web, WWW 2018, pp. 565–574 (2018)

    Google Scholar 

  15. Cheema, G.S., Hakimov, S., Ewerth, R.: Check\_square at CheckThat! 2020: Claim detection in social media via fusion of transformer and syntactic features. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  16. Da San Martino, G., Barrón-Cedeno, A., Wachsmuth, H., Petrov, R., Nakov, P.: SemEval-2020 task 11: detection of propaganda techniques in news articles. In: Proceedings of the 14th Workshop on Semantic Evaluation, SemEval 2020, pp. 1377–1414 (2020)

    Google Scholar 

  17. Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Wong Sak Hoi, G., Zubiaga, A.: SemEval-2017 task 8: RumourEval: determining rumour veracity and support for rumours. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval 2017, pp. 69–76 (2017)

    Google Scholar 

  18. Elsayed, T., et al.: CheckThat! at CLEF 2019: automatic identification and verification of claims. In: Advances in Information Retrieval, pp. 309–315 (2019)

    Google Scholar 

  19. Elsayed, T., et al.: Overview of the CLEF-2019 CheckThat! lab: automatic identification and verification of claims. In: Crestani, F., et al. (eds.) CLEF 2019. LNCS, vol. 11696, pp. 301–321. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28577-7_25

    Chapter  Google Scholar 

  20. Gencheva, P., Nakov, P., Màrquez, L., Barrón-Cedeño, A., Koychev, I.: A context-aware approach for detecting worth-checking claims in political debates. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pp. 267–276 (2017)

    Google Scholar 

  21. Ghanem, B., Glavaš, G., Giachanou, A., Ponzetto, S., Rosso, P., Rangel, F.: UPV-UMA at CheckThat! lab: verifying Arabic claims using cross lingual approach. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2019)

    Google Scholar 

  22. Gorrell, G., et al.SemEval-2019 task 7: rumourEval, determining rumour veracity and support for rumours. In: Proceedings of the 13th International Workshop on Semantic Evaluation, SemEval 2019, pp. 845–854 (2019)

    Google Scholar 

  23. Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: TweetCred: real-time credibility assessment of content on twitter. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 228–243. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13734-6_16

    Chapter  Google Scholar 

  24. Hanselowski, A., et al.: A retrospective analysis of the fake news challenge stance-detection task. In: Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, pp. 1859–1874 (2018)

    Google Scholar 

  25. Hansen, C., Hansen, C., Simonsen, J., Lioma, C.: The Copenhagen team participation in the check-worthiness task of the competition of automatic identification and verification of claims in political debates of the CLEF-2018 fact checking lab. In: Cappellato, L., Ferro, N., Nie, J.Y., Soulier, L. (eds.) Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2018)

    Google Scholar 

  26. Hansen, C., Hansen, C., Simonsen, J., Lioma, C.: Neural weakly supervised fact check-worthiness detection with contrastive sampling-based ranking loss. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2019)

    Google Scholar 

  27. Haouari, F., Hasanain, M., Suwaileh, R., Elsayed, T.: ArCOV-19: the first Arabic COVID-19 Twitter dataset with propagation networks. arXiv preprint arXiv:2004.05861 (2020)

  28. Haouari, F., Hasanain, M., Suwaileh, R., Elsayed, T.: ArCOV19-rumors: arabic COVID-19 Twitter dataset for misinformation detection. arXiv preprint arXiv:2010.08768 (2020)

  29. Hasanain, M., Elsayed, T.: bigIR at CheckThat! 2020: Multilingual BERT for ranking Arabic tweets by check-worthiness. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  30. Hasanain, M., et al.: Overview of CheckThat! 2020 Arabic: automatic identification and verification of claims in social media. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  31. Hasanain, M., Suwaileh, R., Elsayed, T., Barrón-Cedeño, A., Nakov, P.: Overview of the CLEF-2019 CheckThat! lab on automatic identification and verification of claims. Task 2: evidence and factuality. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2019)

    Google Scholar 

  32. Hassan, N., Li, C., Tremayne, M.: Detecting check-worthy factual claims in presidential debates. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1835–1838 (2015)

    Google Scholar 

  33. Hassan, N., Tremayne, M., Arslan, F., Li, C.: Comparing automated factual claim detection against judgments of journalism organizations. In: Computation+Journalism Symposium, pp. 1–5 (2016)

    Google Scholar 

  34. Hassan, N., et al.: ClaimBuster: the first-ever end-to-end fact-checking system. Proc. VLDB Endow. 10(12), 1945–1948 (2017)

    Article  Google Scholar 

  35. Karadzhov, G., Nakov, P., Màrquez, L., Barrón-Cedeño, A., Koychev, I.: Fully automated fact checking using external sources. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pp. 344–353 (2017)

    Google Scholar 

  36. Kartal, Y.S., Kutlu, M.: TOBB ETU at CheckThat! 2020: prioritizing English and Arabic claims based on check-worthiness. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  37. Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI 2016, 3818–3824 (2016)

    Google Scholar 

  38. Martinez-Rico, J., Araujo, L., Martinez-Romo, J.: NLP&IR@UNED at CheckThat! 2020: a preliminary approach for check-worthiness and claim retrieval tasks using neural networks and graphs. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  39. Mihaylova, T., Karadzhov, G., Atanasova, P., Baly, R., Mohtarami, M., Nakov, P.: SemEval-2019 task 8: fact checking in community question answering forums. In: Proceedings of the 13th International Workshop on Semantic Evaluation, SemEval 2019, pp. 860–869 (2019)

    Google Scholar 

  40. Mitra, T., Gilbert, E.: CREDBANK: a large-scale social media corpus with associated credibility annotations. In: Proceedings of the Ninth International AAAI Conference on Web and Social Media, ICWSM 2015, pp. 258–267 (2015)

    Google Scholar 

  41. Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: SemEval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval 2016, pp. 31–41 (2016)

    Google Scholar 

  42. Mukherjee, S., Weikum, G.: Leveraging joint interactions for credibility analysis in news communities. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, pp. 353–362 (2015)

    Google Scholar 

  43. Nakov, P., et al.: Overview of the CLEF-2018 lab on automatic identification and verification of claims in political debates. In: Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, CLEF 2018 (2018)

    Google Scholar 

  44. Nakov, P., et al.: SemEval-2016 Task 3: Community question answering. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval 2015, pp. 525–545 (2016)

    Google Scholar 

  45. Nguyen, V.H., Sugiyama, K., Nakov, P., Kan, M.Y.: FANG: leveraging social context for fake news detection using graph representation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM 2020, p. 1165–1174 (2020)

    Google Scholar 

  46. Nikolov, A., Da San Martino, G., Koychev, I., Nakov, P.: Team\_Alex at CheckThat! 2020: identifying check-worthy tweets with transformer models. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  47. Oshikawa, R., Qian, J., Wang, W.Y.: A survey on natural language processing for fake news detection. In: Proceedings of the 12th Language Resources and Evaluation Conference. pp. 6086–6093. LREC ’20 (2020)

    Google Scholar 

  48. Pogorelov, K., et al.: FakeNews: corona virus and 5G conspiracy task at MediaEval 2020. In: MediaEval 2020 Workshop (2020)

    Google Scholar 

  49. Popat, K., Mukherjee, S., Strötgen, J., Weikum, G.: Credibility assessment of textual claims on the web. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, pp. 2173–2178 (2016)

    Google Scholar 

  50. Shaar, S., Babulkov, N., Da San Martino, G., Nakov, P.: That is a known lie: detecting previously fact-checked claims. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 3607–3618 (2020)

    Google Scholar 

  51. Shaar, S., et al.: Overview of CheckThat! 2020 English: automatic identification and verification of claims in social media. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  52. Shahi, G.K.: AMUSED: An annotation framework of multi-modal social media data. arXiv preprint arXiv:2010.00502 (2020)

  53. Shahi, G.K., Dirkson, A., Majchrzak, T.A.: An exploratory study of COVID-19 misinformation on Twitter. arXiv preprint arXiv:2005.05710 (2020)

  54. Shahi, G.K., Nandini, D.: FakeCovid - a multilingual cross-domain fact check news dataset for COVID-19. In: Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media (2020)

    Google Scholar 

  55. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  56. Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and VERification. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2018, pp. 809–819 (2018)

    Google Scholar 

  57. Touahri, I., Mazroui, A.: EvolutionTeam at CheckThat! 2020: integration of linguistic and sentimental features in a fake news detection approach. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  58. Vasileva, S., Atanasova, P., Màrquez, L., Barrón-Cedeño, A., Nakov, P.: It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP 2019, pp. 1229–1239 (2019)

    Google Scholar 

  59. Williams, E., Rodrigues, P., Novak, V.: Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.) CLEF 2020 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org (2020)

    Google Scholar 

  60. Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: Early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015, pp. 1395–1405 (2015)

    Google Scholar 

  61. Zubiaga, A., Liakata, M., Procter, R., Hoi, G.W.S., Tolmie, P.: Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS ONE 11(3), e0150989 (2016)

    Article  Google Scholar 

  62. Zuo, C., Karakas, A., Banerjee, R.: A hybrid recognition system for check-worthy claims using heuristics and supervised learning. In: Cappellato, L., Ferro, N., Nie, J.Y., Soulier, L. (eds.) Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings. CEUR-WS.org (2018)

    Google Scholar 

Download references

Acknowledgments

The work of Tamer Elsayed and Maram Hasanain is made possible by NPRP grant #NPRP-11S-1204-170060 from the Qatar National Research Fund (a member of Qatar Foundation). The work of Fatima Haouari is supported by GSRA grant #GSRA6-1-0611-19074 from the Qatar National Research Fund. The statements made herein are solely the responsibility of the authors.

This research is also part of the Tanbih mega-project, developed at the Qatar Computing Research Institute, HBKU, which aims to limit the effect of “fake news”, propaganda, and media bias.

Author information

Authors and Affiliations

  1. Qatar Computing Research Institute, HBKU, Ar-Rayyan, Qatar

    Preslav Nakov, Shaden Shaar & Firoj Alam

  2. University of Padova, Padova, Italy

    Giovanni Da San Martino

  3. Qatar University, Doha, Qatar

    Tamer Elsayed, Fatima Haouari & Maram Hasanain

  4. DIT, Università di Bologna, Forlì, Italy

    Alberto Barrón-Cedeño

  5. Sofia University, Sofia, Bulgaria

    Nikolay Babulkov & Alex Nikolov

  6. Newtral Media Audiovisual, Madrid, Spain

    Rubén Míguez

  7. University of Duisburg-Essen, Duisburg, Germany

    Gautam Kishore Shahi

  8. University of Applied Sciences Potsdam, Potsdam, Germany

    Julia Maria Struß

  9. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

Authors
  1. Preslav Nakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giovanni Da San Martino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tamer Elsayed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alberto Barrón-Cedeño
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rubén Míguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shaden Shaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Firoj Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fatima Haouari
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maram Hasanain
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nikolay Babulkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alex Nikolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gautam Kishore Shahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Julia Maria Struß
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Thomas Mandl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Preslav Nakov .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nakov, P. et al. (2021). The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_75

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72240-1_75

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation