Abstract
During the COVID-19 outbreak, fake news regarding the disease have spread at an increasing rate. Let’s think, for instance, to face masks wearing related news or various home-made treatments to cure the disease. To contrast this phenomenon, the fact-checking community has intensified its efforts by producing a large number of fact-checking reports. In this work, we focus on empowering knowledge-based approaches for misinformation identification with previous knowledge gathered from existing fact-checking reports. Very few works in literature have exploited the information regarding claims that have been already fact-checked. The main idea that we explore in this work is to exploit the detailed information in the COVID-19 fact check reports in order to create an extended Knowledge Graph. By analysing the graph information about the already checked claims, we can verify newly coming content more effectively. Another gap that we aim to fill is the temporal representation of the facts stored in the knowledge graph. At the best of our knowledge, this is the first attempt to associate the temporal validity to the KG relations. This additional information can be used to further enhance the validation of claims.
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Available at the address: https://factcheck.afp.com/http%253A%252F%252Fdoc.afp.com%252F9VY4M3-1.
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Permanent copy available at the address: https://web.archive.org/web/20220315225545/https://www.snopes.com/fact-check/pfizer-covid-19-vaccine-fetal-cells/.
References
Hogan, A., et. al.: Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge, vol. 12, no. 2, pp. 1–257 (2021). https://doi.org/10.2200/S01125ED1V01Y202109DSK022
Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: SpaCy: Industrial-Strength Natural Language Processing in Python (2020). https://doi.org/10.5281/zenodo.1212303
Hudson, R.P., msg systems ag: coreferee: coreference resolution for multiple languages. https://github.com/msg-systems/coreferee
Huggingface: neuralcoref: fast coreference resolution in spaCy with neural networks. https://github.com/huggingface/neuralcoref
Mihaylova, T., et. al.: Fact checking in community forums. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Pérez-Rosas, V., Mihalcea, R.: Experiments in open domain deception detection. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1120–1125 (2015)
Shaar, S., Martino, G.D.S., Babulkov, N., Nakov, P.: That is a known lie: detecting previously fact-checked claims. arXiv preprint arXiv:2005.06058 (2020)
Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. Knowl. Based Syst. 104, 123–133 (2016)
Shiralkar, P., Flammini, A., Menczer, F., Ciampaglia, G.L.: Finding streams in knowledge graphs to support fact checking. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 859–864. IEEE (2017)
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)
Tchechmedjiev, A., et al.: ClaimsKG: a knowledge graph of fact-checked claims. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 309–324. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_20
Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. (CSUR) 53(5), 1–40 (2020)
Acknowledgements
This work is partly supported by the “AI-Info Communication Study (AIS) Scheme 2021/22 (Ref. AIS 21–22/05)” and “Teaching Development Grant” - School of Communication and Film, Hong Kong Baptist University, Hong Kong, China.
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Mengoni, P., Yang, J. (2022). Empowering COVID-19 Fact-Checking with Extended Knowledge Graphs. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_10
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