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Distributed Architecture for Fake News Detection

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1267)


Countering the fake news phenomenon has become one of the most important challenges for democratic societies, governments and non-profit organizations, as well as for the researchers coming from several domains. This is not a local problem, and demands a holistic approach to analyzing heterogeneous data and storing the results. The major contribution of this paper is the proposition of an innovative distributed architecture to tackle the above-mentioned problems. The architecture uses state-of-the-art technologies with focus on efficiency, scalability and also openness, so that community-created components and analyzers could be added.


  • Fake news detection
  • Distributed architecture
  • Machine learning
  • Deep learning

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  • DOI: 10.1007/978-3-030-57805-3_20
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This work is funded under SocialTruth project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477.

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Correspondence to Rafał Kozik .

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Kozik, R., Choraś, M., Kula, S., Pawlicki, M. (2021). Distributed Architecture for Fake News Detection. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020). CISIS 2019. Advances in Intelligent Systems and Computing, vol 1267. Springer, Cham.

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