A Novel Stance Labeling Algorithm for Misinformation Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-58604-0_29
Reference23 articles.
1. Tufchi, S., Yadav, A., Ahmed, T.: A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunities. Int. J. Multimed. Inf. Retr. 12(2) (2023). https://doi.org/10.1007/s13735-023-00296-3
2. Ghorbanpour, F., Ramezani, M., Fazli, M.A., Rabiee, H.R.: FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media. Soc. Netw. Anal. Min. 13(1) (2023). https://doi.org/10.1007/s13278-023-01065-0
3. Barve, Y., Saini, J.R., Kotecha, K., Gaikwad, H.: Detecting and fact- checking misinformation using ‘veracity scanning model.’ Int. J. Adv. Comput. Sci. Appl. 13(2), 201–209 (2022). https://doi.org/10.14569/IJACSA.2022.0130225
4. Barve, Y., Saini, J.R.: Healthcare misinformation detection and fact-checking : a novel approach. Int. J. Adv. Comput. Sci. Appl. 12(10), 295–303 (2021)
5. Aljrees, T., et al.: Fake news stance detection using selective features and FakeNET. PLoS One 18(7) July (2023). https://doi.org/10.1371/journal.pone.0287298
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