Mining Text Patterns over Fake and Real Tweets-Reference-Cited by-同舟云学术

Mining Text Patterns over Fake and Real Tweets

Published:2020 Issue: Volume: Page:648-660
ISSN:1865-0929
Container-title:Information Processing and Management of Uncertainty in Knowledge-Based Systems
language:
Short-container-title:

Author:

Diaz-Garcia Jose A.^ORCID,Fernandez-Basso Carlos^ORCID,Ruiz M. Dolores^ORCID,Martin-Bautista Maria J.^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-50143-3_51

Reference18 articles.

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2. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

3. de Alfaro, L., et al.: Identifying fake news from Twitter sharing data: a large-scale study. coRR abs 1902.07207 (2019)

4. Amador Diaz Lopez, J., Oehmichen, A., Molina-Solana, M.: Fakenews on 2016 US elections viral tweets (November 2016–March 2017), November 2017. https://doi.org/10.5281/zenodo.1048826

5. Berzal, F., Blanco, I., Sánchez, D., Vila, M.A.: Measuring the accuracy and interest of association rules: a new framework. Intell. Data Anal. 6(3), 221–235 (2002)

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