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

Panagiotis Kasnesis; Ryan Heartfield; Lazaros Toumanidis; Xing Liang; George Loukas; Charalampos Patrikakis

doi:10.1109/ICMEW46912.2020.9106044

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

Panagiotis Kasnesis
, Ryan Heartfield
, Lazaros Toumanidis
, Xing Liang
, George Loukas
, Charalampos Patrikakis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE)
DOIs	https://doi.org/10.1109/ICMEW46912.2020.9106044
Publication status	Published - 7 Jul 2020

Bibliographical note

Note: Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859

This work was supported by EUNOMIA project [Grant Number: 825171].

Organising Body: Institute of Electrical and Electronics Engineers (IEEE)

Organising Body: Institute of Electrical and Electronics Engineers (IEEE)

Keywords

Information cascade
Clustering
Deep learning
Paraphrase Identification
Computer science and informatics

Access to Document

10.1109/ICMEW46912.2020.9106044

https://dx.doi.org/10.1109/ICMEW46912.2020.9106044

1 Paper

A prototype deep learning paraphrase identification service for discovering information cascades in social networks
Kasnesis, P., Heartfield, R., Toumanidis, L., Liang, X., Loukas, G. & Patrikakis, C., 7 Jul 2020.
Research output: Contribution to conference › Paper › peer-review

Cite this

Kasnesis, P., Heartfield, R., Toumanidis, L., Liang, X., Loukas, G., & Patrikakis, C. (2020). A prototype deep learning paraphrase identification service for discovering information cascades in social networks. In Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE) https://doi.org/10.1109/ICMEW46912.2020.9106044

Kasnesis, Panagiotis ; Heartfield, Ryan ; Toumanidis, Lazaros et al. / A prototype deep learning paraphrase identification service for discovering information cascades in social networks. Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE). 2020.

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author = "Panagiotis Kasnesis and Ryan Heartfield and Lazaros Toumanidis and Xing Liang and George Loukas and Charalampos Patrikakis",

note = "Note: Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE)",

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Kasnesis, P, Heartfield, R, Toumanidis, L, Liang, X, Loukas, G & Patrikakis, C 2020, A prototype deep learning paraphrase identification service for discovering information cascades in social networks. in Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICMEW46912.2020.9106044

A prototype deep learning paraphrase identification service for discovering information cascades in social networks. / Kasnesis, Panagiotis; Heartfield, Ryan; Toumanidis, Lazaros et al.
Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE). 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A prototype deep learning paraphrase identification service for discovering information cascades in social networks

AU - Kasnesis, Panagiotis

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AU - Liang, Xing

AU - Loukas, George

AU - Patrikakis, Charalampos

N1 - Note: Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE)

PY - 2020/7/7

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N2 - 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.

AB - 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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KW - Clustering

KW - Deep learning

KW - Paraphrase Identification

KW - Computer science and informatics

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DO - 10.1109/ICMEW46912.2020.9106044

M3 - Conference contribution

BT - Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE)

ER -

Kasnesis P, Heartfield R, Toumanidis L, Liang X, Loukas G, Patrikakis C. A prototype deep learning paraphrase identification service for discovering information cascades in social networks. In Published in: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2020. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781728114859 This work was supported by EUNOMIA project [Grant Number: 825171]. Organising Body: Institute of Electrical and Electronics Engineers (IEEE) Organising Body: Institute of Electrical and Electronics Engineers (IEEE). 2020 doi: 10.1109/ICMEW46912.2020.9106044

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

Abstract

Bibliographical note

Keywords

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Research output

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

Cite this