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A prototype deep learning paraphrase identification service for discovering information cascades in social networks

A prototype deep learning paraphrase identification service for discovering information cascades in social networks

Kasnesis, Panagiotis, Heartfield, Ryan, Toumanidis, Lazaros, Liang, Xing, Loukas, George and Patrikakis, Charalampos Z. (2020) A prototype deep learning paraphrase identification service for discovering information cascades in social networks. In: IEEE International Conference on Multimedia and Expo. IEEE, pp. 1-4. ISBN 978-1728114859 (doi:https://doi.org/10.1109/ICMEW46912.2020.9106044)

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Abstract

Identifying the provenance of information posted on social media and how this information may have changed over time can be very helpful in assessing its trustworthiness. Here, we introduce a novel mechanism for discovering “post-based” information cascades, including the earliest relevant post and how its information has evolved over subsequent posts. Our prototype leverages multiple innovations in the combination of dynamic data sub-sampling and multiple natural language processing and analysis techniques, benefiting from deep learning architectures. We evaluate its performance on EMTD, a dataset that we have generated from our private experimental instance of the decentralised social network Mastodon, as well as the benchmark Microsoft Research Paraphrase Corpus, reporting no errors in sub-sampling based on clustering, and an average accuracy of 92% and F1 score of 93% for paraphrase identification.

Item Type: Conference Proceedings
Title of Proceedings: IEEE International Conference on Multimedia and Expo
Uncontrolled Keywords: Information trustworthiness, information cascade
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Faculty of Liberal Arts & Sciences > School of Computing and Mathematical Sciences
Last Modified: 03 Jul 2020 14:24
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/28150

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