Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

The CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection

verfasst von : Preslav Nakov, Alberto Barrón-Cedeño, Giovanni Da San Martino, Firoj Alam, Julia Maria Struß, Thomas Mandl, Rubén Míguez, Tommaso Caselli, Mucahid Kutlu, Wajdi Zaghouani, Chengkai Li, Shaden Shaar, Gautam Kishore Shahi, Hamdy Mubarak, Alex Nikolov, Nikolay Babulkov, Yavuz Selim Kartal, Javier Beltrán

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The fifth edition of the CheckThat! Lab is held as part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting various factuality tasks in seven languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 focuses on disinformation related to the ongoing COVID-19 infodemic and politics, and asks to predict whether a tweet is worth fact-checking, contains a verifiable factual claim, is harmful to the society, or is of interest to policy makers and why. Task 2 asks to retrieve claims that have been previously fact-checked and that could be useful to verify the claim in a tweet. Task 3 is to predict the veracity of a news article. Tasks 1 and 3 are classification problems, while Task 2 is a ranking one.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Advancing Math-Aware Search: The ARQMath-3 Lab at CLEF 2022
Nächstes Kapitel BioASQ at CLEF2022: The Tenth Edition of the Large-scale Biomedical Semantic Indexing and Question Answering Challenge
Fußnoten
Literatur
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)
2.
Alam, F., et al.: Fighting the COVID-19 infodemic in social media: a holistic perspective and a call to arms. In: Proceedings of the International AAAI Conference on Web and Social Media, ICWSM 2021, pp. 913–922 (2021)
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. In: Findings of EMNLP 2021, pp. 611–649 (2021)
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 et al. [16]
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 et al. [15]
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)
7.
Balouchzahi, F., Shashirekha, H., Sidorov, G.: MUCIC at CheckThat! 2021:FaDo-fake news detection and domain identification using transformersensembling. In: Faggioli et al. [26], pp. 455–464D
8.
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)
9.
10.
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)
11.
12.
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 et al. [16]
13.
Bouziane, M., Perrin, H., Cluzeau, A., Mardas, J., Sadeq, A.: Buster.AI at CheckThat! 2020: Insights and recommendations to improve fact-checking. In: Cappellato et al. [14]
14.
Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A. (eds.): CLEF 2020 Working Notes. CEUR Workshop Proceedings (2020)
15.
Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.): Working Notes of CLEF 2019 Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings (2019)
16.
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 (2018)
17.
Cheema, G.S., Hakimov, S., Ewerth, R.: Check_square at CheckThat! 2020: claim detection in social media via fusion of transformer and syntacticfeatures. In: Cappellato et al. [14]
18.
Chernyavskiy, A., Ilvovsky, D., Nakov, P.: Aschern at CLEF CheckThat! 2021: lambda-calculus of fact-checked claims. In: Faggioli et al. [26]
19.
Cusmuliuc, C.G., Amarandei, M.A., Pelin, I., Cociorva, V.I., Iftene, A.: UAICS at CheckThat! 2021: fake news detection. In: Faggioli et al. [26]
20.
Da San Martino, G., Barrón-Cedeño, A., Nakov, P.: Findings of the NLP4IF-2019 shared task on fine-grained propaganda detection. In: Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda. NLP4IF 2019, pp. 162–170 (2019)
21.
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)
22.
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)
23.
Dimitrov, D., et al.: SemEval-2021 task 6: detection of persuasion techniques in texts and images. In: Proceedings of the International Workshop on Semantic Evaluation. SemEval 2021, pp. 70–98 (2021)
24.
Elsayed, T., et al.: CheckThat! at CLEF 2019: Automatic identification and verification of claims. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) Advances in Information Retrieval, pp. 309–315. Springer International Publishing, Cham (2019)CrossRef
25.
26.
Faggioli, G., Ferro, N., Joly, A., Maistro, M., Piroi, F. (eds.): CLEF 2021 Working Notes. Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum (2021)
27.
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)
28.
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 et al. [15]
29.
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)
30.
31.
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)
32.
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 et al. [16]
33.
Hansen, C., Hansen, C., Simonsen, J., Lioma, C.: Neural weakly supervised fact check-worthiness detection with contrastive sampling-based ranking loss. In: Cappellato et al. [15]
34.
Hasanain, M., Elsayed, T.: bigIR at CheckThat! 2020: multilingual BERT for ranking Arabic tweets by check-worthiness. In: Cappellato et al. [14]
35.
Hasanain, M., et al.: Overview of CheckThat! 2020 Arabic: automatic identification and verification of claims in social media. In: Cappellato et al. [14]
36.
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 et al. [15]
37.
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)
38.
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)
39.
Hassan, N., et al.: ClaimBuster: the first-ever end-to-end fact-checking system. Proc. VLDB Endowment 10(12), 1945–1948 (2017)CrossRef
40.
Jaradat, I., Gencheva, P., Barrón-Cedeño, A., Màrquez, L., Nakov, P.: ClaimRank: detecting check-worthy claims in Arabic and English. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. NAACL-HLT 2018, pp. 26–30 (2018)
41.
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)
42.
Kartal, Y.S., Kutlu, M.: TOBB ETU at CheckThat! 2020: prioritizing English and Arabic claims based on check-worthiness. In: Cappellato et al. [14]
43.
Kazemi, A., Garimella, K., Gaffney, D., Hale, S.: Claim matching beyond English to scale global fact-checking. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. ACL-IJCNLP 202, pp. 4504–45171 (2021)
44.
Kovachevich, N.: BERT fine-tuning approach to CLEF CheckThat! fake news detection. In: Faggioli et al. [26]
45.
Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence. IJCAI 2016, pp. 3818–3824 (2016)
46.
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 et al. [14]
47.
Mihaylova, S., Borisova, I., Chemishanov, D., Hadzhitsanev, P., Hardalov, M., Nakov, P.: DIPS at CheckThat! 2021: verified claim retrieval. In: Faggioli et al. [26]
48.
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)
49.
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)
50.
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)
51.
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)
52.
Nakov, P., Alam, F., Shaar, S., Da San Martino, G., Zhang, Y.: COVID-19 in Bulgarian social media: factuality, harmfulness, propaganda, and framing. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing. RANLP 2021, pp. 997–1009 (2021)
53.
Nakov, P., Alam, F., Shaar, S., Da San Martino, G., Zhang, Y.: A second pandemic? Analysis of fake news about COVID-19 vaccines in Qatar. In: Proceedings of Conference on Recent Advances in Natural Language Processing, pp. 1010–1021 (2021)
54.
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)
55.
Nakov, P., et al.: Automated fact-checking for assisting human fact-checkers. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. IJCAI 2021, pp. 4551–4558 (2021)
56.
Nakov, P., et al.: The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 639–649. Springer, Cham (2021). https://​doi.​org/​10.​1007/​978-3-030-72240-1_​75CrossRef
57.
58.
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)
59.
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, pp. 1165–1174 (2020)
60.
Nikolov, A., Da San Martino, G., Koychev, I., Nakov, P.: Team_Alex at CheckThat! 2020: identifying check-worthy tweets with transformer models. In: Cappellato et al. [14]
61.
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. LREC 2020, pp. 6086–6093 (2020)
62.
Pogorelov, K., et al.: FakeNews: corona virus and 5G conspiracy task at MediaEval 2020. In: Proceedings of the MediaEval 2020 Workshop. MediaEval 2020 (2020)
63.
64.
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)
65.
Pritzkau, A.: NLytics at CheckThat! 2021: check-worthiness estimation as a regression problem on transformers. In: Faggioli et al. [26]
66.
Sepúlveda-Torres, R., Saquete, E.: GPLSI team at CLEF CheckThat! 2021: fine-tuning BETO and RoBERTa. In: Faggioli et al. [26]
67.
Shaar, S., Alam, F., Da San Martino, G., Nakov, P.: The role of context in detecting previously fact-checked claims. arXiv:​2104.​07423 (2021)
68.
Shaar, S., et al.: Findings of the NLP4IF-2021 shared tasks on fighting the COVID-19 infodemic and censorship detection. In: Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda. NLP4IF 2021, pp. 82–92 (2021)
69.
Shaar, S., Alam, F., Martino, G.D.S., Nakov, P.: Assisting the human fact-checkers: detecting all previously fact-checked claims in a document. arXiv preprint arXiv:​2109.​07410 (2021)
70.
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)
71.
Shaar, S., et al.: Overview of the CLEF-2021 CheckThat! lab task 2 on detecting previously fact-checked claims in tweets and political debates. In: Faggioli et al. [26]
72.
Shaar, S., et al.: Overview of the CLEF-2021 CheckThat! lab task 1 on check-worthiness estimation in tweets and political debates. In: Faggioli et al. [26]
73.
Shaar, S., et al.: Overview of CheckThat! 2020 English: automatic identification and verification of claims in social media. In: Cappellato et al. [14]
74.
75.
Shahi, G.K., Dirkson, A., Majchrzak, T.A.: An exploratory study of COVID-19 misinformation on Twitter. Online Social Networks Media 22, 100104 (2021)CrossRef
76.
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)
77.
Shahi, G.K., Struß, J.M., Mandl, T.: Overview of the CLEF-2021 CheckThat! lab: task 3 on fake news detection. In: Faggioli et al. [26]
78.
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)CrossRef
79.
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)
80.
Touahri, I., Mazroui, A.: EvolutionTeam at CheckThat! 2020: integration of linguistic and sentimental features in a fake news detection approach. In: Cappellato et al. [14]
81.
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)
82.
Williams, E., Rodrigues, P., Tran, S.: Accenture at CheckThat! 2021: interesting claim identification and ranking with contextually sensitive lexical training data augmentation. In: Faggioli et al. [14]
83.
Williams, E., Rodrigues, P., Tran, S.: Accenture at CheckThat! 2021: interesting claim identification and ranking with contextually sensitive lexical training data augmentation. In: Faggioli et al. [26]
84.
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)
85.
Zhou, X., Wu, B., Fung, P.: Fight for 4230 at CLEF CheckThat! 2021: domain-specific preprocessing and pretrained model for ranking claims by check-worthiness. In: Faggioli et al. [26]
86.
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)CrossRef
87.
Zuo, C., Karakas, A., Banerjee, R.: A hybrid recognition system for check-worthy claims using heuristics and supervised learning. In: Cappellato et al. [16]
Metadaten
Titel
The CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection
verfasst von
Preslav Nakov
Alberto Barrón-Cedeño
Giovanni Da San Martino
Firoj Alam
Julia Maria Struß
Thomas Mandl
Rubén Míguez
Tommaso Caselli
Mucahid Kutlu
Wajdi Zaghouani
Chengkai Li
Shaden Shaar
Gautam Kishore Shahi
Hamdy Mubarak
Alex Nikolov
Nikolay Babulkov
Yavuz Selim Kartal
Javier Beltrán
Copyright-Jahr
2022
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_52

Neuer Inhalt

    Bildnachweise