Rumor detection on twitter with tree-structured recursive neural networks

Jing Ma; Wei Gao; Kam Fai Wong

doi:10.18653/v1/p18-1184

Rumor detection on twitter with tree-structured recursive neural networks

Jing Ma
, Wei Gao
, Kam Fai Wong

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

691 Citations (Scopus)

Abstract

Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.

Original language	English
Title of host publication	Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	Long Papers
Editors	Iryna Gurevych, Yusuke Miyao
Place of Publication	Melbourne
Publisher	Association for Computational Linguistics (ACL)
Pages	1980-1989
Number of pages	10
Volume	1
ISBN (Electronic)	9781948087322
DOIs	https://doi.org/10.18653/v1/p18-1184
Publication status	Published - Jul 2018
Event	56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 https://acl2018.org/ (Conference website) https://acl2018.org/programme/schedule/ (Conference program) https://aclanthology.org/volumes/P18-1/ (Conference proceeding)

Publication series

Name	Proceedings of Annual Meeting of the Association for Computational Linguistics
Publisher	Association for Computational Linguistics

Conference

Conference	56th Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2018
Country/Territory	Australia
City	Melbourne
Period	15/07/18 → 20/07/18
Internet address	https://acl2018.org/ (Conference website) https://acl2018.org/programme/schedule/ (Conference program) https://aclanthology.org/volumes/P18-1/ (Conference proceeding)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

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10.18653/v1/p18-1184Licence: CC BY

Cite this

Ma, J., Gao, W., & Wong, K. F. (2018). Rumor detection on twitter with tree-structured recursive neural networks. In I. Gurevych, & Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Long Papers (Vol. 1, pp. 1980-1989). (Proceedings of Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1184

Ma, Jing ; Gao, Wei ; Wong, Kam Fai. / Rumor detection on twitter with tree-structured recursive neural networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Long Papers. editor / Iryna Gurevych ; Yusuke Miyao. Vol. 1 Melbourne : Association for Computational Linguistics (ACL), 2018. pp. 1980-1989 (Proceedings of Annual Meeting of the Association for Computational Linguistics).

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title = "Rumor detection on twitter with tree-structured recursive neural networks",

abstract = "Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.",

author = "Jing Ma and Wei Gao and Wong, \{Kam Fai\}",

note = "This work is partly supported by Innovation and Technology Fund (ITF) Project No. 6904333, and General Research Fund (GRF) Project No. 14232816 (12183516). Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

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Ma, J, Gao, W & Wong, KF 2018, Rumor detection on twitter with tree-structured recursive neural networks. in I Gurevych & Y Miyao (eds), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Long Papers. vol. 1, Proceedings of Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), Melbourne, pp. 1980-1989, 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Victoria, Australia, 15/07/18. https://doi.org/10.18653/v1/p18-1184

Rumor detection on twitter with tree-structured recursive neural networks. / Ma, Jing; Gao, Wei; Wong, Kam Fai.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Long Papers. ed. / Iryna Gurevych; Yusuke Miyao. Vol. 1 Melbourne: Association for Computational Linguistics (ACL), 2018. p. 1980-1989 (Proceedings of Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N1 - This work is partly supported by Innovation and Technology Fund (ITF) Project No. 6904333, and General Research Fund (GRF) Project No. 14232816 (12183516). Publisher Copyright: © 2018 Association for Computational Linguistics

PY - 2018/7

Y1 - 2018/7

N2 - Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.

AB - Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.

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Ma J, Gao W, Wong KF. Rumor detection on twitter with tree-structured recursive neural networks. In Gurevych I, Miyao Y, editors, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Long Papers. Vol. 1. Melbourne: Association for Computational Linguistics (ACL). 2018. p. 1980-1989. (Proceedings of Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/p18-1184

Rumor detection on twitter with tree-structured recursive neural networks

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