nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

FakeClaim: A Multiple Platform-Driven Dataset for Identification of Fake News on 2023 Israel-Hamas War

verfasst von : Gautam Kishore Shahi, Amit Kumar Jaiswal, Thomas Mandl

Erschienen in: Advances in Information Retrieval

Verlag: Springer Nature Switzerland

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion.

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 Federated Conversational Recommender Systems

Nächstes Kapitel Countering Mainstream Bias via End-to-End Adaptive Local Learning

We refer X as Twitter for clarity.

https://textblob.readthedocs.io/en/dev/quickstart.html.

https://huggingface.co/roberta-base.

Lovelace, A.G.: Tomorrow’s wars and the media. The US Army War College Quarterly: Parameters 52(2), 117–134 (2022)

Shahi, G.K., Nandini, D.: FakeCovid–a multilingual cross-domain fact check news dataset for COVID-19. arXiv preprint arXiv:2006.11343 (2020)

Shahi, G.K., Majchrzak, T.A.: AMUSED: an annotation framework of multimodal social media data. In: Sanfilippo, F., Granmo, O.C., Yayilgan, S.Y., Bajwa, I.S. (eds.) Intelligent Technologies and Applications. INTAP 2021. CCIS, vol. 1616. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10525-8_23

Cer, D., et al.: Universal sentence encoder for English. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP: System Demonstrations, Brussels, Belgium, October 31–November 4, pp. 169–174. Association for Computational Linguistics (2018)

Fatima, K. Salem, A. Feel, R.A. Elbassuoni, S. Jaber, M., Farah, M.: FA-KES: a fake news dataset around the Syrian war. In: Proceedings of the Thirteenth International Conference on Web and Social Media, ICWSM 2019, Munich, Germany, June 11–14, 2019, pp. 573–582. AAAI Press (2019)

Chen, E., Ferrara, E.: Tweets in time of conflict: a public dataset tracking the Twitter discourse on the war between Ukraine and Russia. In: Proceedings of the Seventeenth International AAAI Conference on Web and Social Media, ICWSM 2023, June 5–8, 2023, Limassol, Cyprus, pp. 1006–1013. AAAI Press (2023)

Pierri, F., Luceri, L., Jindal, N., Ferrara, E.: Propaganda and misinformation on Facebook and Twitter during the Russian Invasion of Ukraine. In: Proceedings of the 15th ACM Web Science Conference, WebSci, Austin, TX, USA, 30 April 2023–1 May, pp. 65–74. ACM (2023)

Zhu, Y., Haq, E., Lee, L., Tyson, G., Hui, P.: A Reddit dataset for the Russo-Ukrainian conflict in 2022. arXiv preprint arXiv:2206.05107 (2022)

Shin, Y., Sojdehei, Y., Zheng, L., Blanchard, B.: Content-based unsupervised fake news detection on Ukraine-Russia war. SMU Data Sci. Rev. 7(1), 3 (2023)

10.

Köhler, J., et al.: Overview of the CLEF-2022 checkthat! lab: task 3 on fake news detection. In: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, Sept. 5th–8th, pp. 404–421. CEUR-WS.org (2022)

11.

Shahi, G.K., Struß, J.M., Mandl, T.: Overview of the CLEF-2021 checkthat! lab: task 3 on fake news detection. In: Proceedings of the Working Notes of CLEF - Conference and Labs of the Evaluation Forum, Bucharest, Romania, September 21–24th, volume 2936 of CEUR Workshop Proceedings, pp. 406–423. CEUR-WS.org, (2021)

12.

Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)

13.

Röchert, D., Shahi, G.K., Neubaum, G., Ross, B., Stieglitz, S.: The networked context of COVID-19 misinformation: Informational homogeneity on Youtube at the beginning of the pandemic. Online Soc. Netw. Media 26, 100164 (2021)

14.

Wu, J., Hooi, B.: Decor: degree-corrected social graph refinement for fake news detection. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2582–2593 (2023)

15.

Shahi, G.K., Dirkson, A., Majchrzak, T.: An exploratory study of COVID-19 misinformation on Twitter. Online Soc. Netw. Media 22, 100104 (2021)CrossRef

16.

Ajao, O., Bhowmik, D., Zargari, Z.: Fake news identification on Twitter with hybrid CNN and RNN models. In: Proceedings of the 9th International Conference on Social Media and Society, SMSociety 2018, Copenhagen, Denmark, July 18–20, 2018, pp. 226–230. ACM (2018)

17.

Shazeer, N., Doherty, R., Evans, C., Waterson, C.: Swivel: improving embeddings by noticing what’s missing. arXiv preprint arXiv:1602.02215 (2016)

18.

Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

19.

Bommasani, R., et al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)

20.

Jaiswal, A., Liu, H.: Lightweight adaptation of neural language models via subspace embedding. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 3968–3972 (2023)

Titel: FakeClaim: A Multiple Platform-Driven Dataset for Identification of Fake News on 2023 Israel-Hamas War
verfasst von: Gautam Kishore Shahi
Amit Kumar Jaiswal
Thomas Mandl
Verlag: Springer Nature Switzerland
Buch: Advances in Information Retrieval
Print ISBN: 978-3-031-56068-2

Electronic ISBN: 978-3-031-56069-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56069-9_5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner