New paper accepted: “A prototype deep learning paraphrase identification service for discovering information cascades in social networks”

Our paper will be presented at IEEE International Conference on Multimedia and Expo (ICME):

Kasnesis, P., Heartfield, R., Toumanidis, L., Liang, X., Loukas, G. and Patrikakis, C.Z., 2020. A prototype deep learning paraphrase identification service for discovering information cascades in social networks. IEEE ICME, London, 6-10 July 2020.

Its 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.”

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