Abstract
The automatic detection of rumors in social networks is an important problem that would allow counteracting the effects that the propagation of false information produces. We study the performance of deep learning architectures in this problem, analyzing ten different machines on word2vec and BERT. Our results show that some architectures are more suitable for some particular classes, suggesting that the use of committee machines would offer advantages in this task.
Keywords
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Acknowledgement
Mr. Mendoza acknowledge funding from the Millennium Institute for Foundational Research on Data. Mr. Mendoza was also funded by ANID PIA/APOYO AFB180002 and ANID FONDECYT REGULAR 1200211.
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Providel, E., Mendoza, M. (2020). Using Deep Learning to Detect Rumors in Twitter. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_22
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DOI: https://doi.org/10.1007/978-3-030-49570-1_22
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