Rumour Veracity Estimation with Deep Learning for Twitter

Jyoti Prakash Singh; Nripendra P. Rana; Yogesh K. Dwivedi

doi:10.1007/978-3-030-20671-0_24

Rumour Veracity Estimation with Deep Learning for Twitter

Jyoti Prakash Singh
, Nripendra P. Rana^*
, Yogesh K. Dwivedi

^*Corresponding author for this work

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

13 Scopus citations

Abstract

Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.

Original language	English
Title of host publication	ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings
Editors	Yogesh Dwivedi, Emmanuel Ayaburi, Richard Boateng, John Effah
Publisher	Springer Science and Business Media, LLC
Pages	351-363
Number of pages	13
ISBN (Print)	9783030206703
DOIs	https://doi.org/10.1007/978-3-030-20671-0_24
State	Published - 2019
Externally published	Yes
Event	IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019 - Accra, Ghana Duration: 21 Jun 2019 → 22 Jun 2019

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	558
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019
Country/Territory	Ghana
City	Accra
Period	21/06/19 → 22/06/19

Bibliographical note

Publisher Copyright:
© 2019, IFIP International Federation for Information Processing.

Keywords

Deep learning
Machine learning
Neural network
Rumour veracity
Twitter

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1007/978-3-030-20671-0_24

Cite this

Singh, J. P., Rana, N. P., & Dwivedi, Y. K. (2019). Rumour Veracity Estimation with Deep Learning for Twitter. In Y. Dwivedi, E. Ayaburi, R. Boateng, & J. Effah (Eds.), ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings (pp. 351-363). (IFIP Advances in Information and Communication Technology; Vol. 558). Springer Science and Business Media, LLC. https://doi.org/10.1007/978-3-030-20671-0_24

Singh, Jyoti Prakash ; Rana, Nripendra P. ; Dwivedi, Yogesh K. / Rumour Veracity Estimation with Deep Learning for Twitter. ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings. editor / Yogesh Dwivedi ; Emmanuel Ayaburi ; Richard Boateng ; John Effah. Springer Science and Business Media, LLC, 2019. pp. 351-363 (IFIP Advances in Information and Communication Technology).

@inproceedings{04c5e66c18764b79a3607bfaef82cd75,

title = "Rumour Veracity Estimation with Deep Learning for Twitter",

abstract = "Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.",

keywords = "Deep learning, Machine learning, Neural network, Rumour veracity, Twitter",

author = "Singh, \{Jyoti Prakash\} and Rana, \{Nripendra P.\} and Dwivedi, \{Yogesh K.\}",

note = "Publisher Copyright: {\textcopyright} 2019, IFIP International Federation for Information Processing.; IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019 ; Conference date: 21-06-2019 Through 22-06-2019",

year = "2019",

doi = "10.1007/978-3-030-20671-0\_24",

language = "English",

isbn = "9783030206703",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer Science and Business Media, LLC",

pages = "351--363",

editor = "Yogesh Dwivedi and Emmanuel Ayaburi and Richard Boateng and John Effah",

booktitle = "ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings",

}

Singh, JP, Rana, NP & Dwivedi, YK 2019, Rumour Veracity Estimation with Deep Learning for Twitter. in Y Dwivedi, E Ayaburi, R Boateng & J Effah (eds), ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings. IFIP Advances in Information and Communication Technology, vol. 558, Springer Science and Business Media, LLC, pp. 351-363, IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Accra, Ghana, 21/06/19. https://doi.org/10.1007/978-3-030-20671-0_24

Rumour Veracity Estimation with Deep Learning for Twitter. / Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.
ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings. ed. / Yogesh Dwivedi; Emmanuel Ayaburi; Richard Boateng; John Effah. Springer Science and Business Media, LLC, 2019. p. 351-363 (IFIP Advances in Information and Communication Technology; Vol. 558).

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

TY - GEN

T1 - Rumour Veracity Estimation with Deep Learning for Twitter

AU - Singh, Jyoti Prakash

AU - Rana, Nripendra P.

AU - Dwivedi, Yogesh K.

PY - 2019

Y1 - 2019

N2 - Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.

AB - Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.

KW - Deep learning

KW - Machine learning

KW - Neural network

KW - Rumour veracity

KW - Twitter

UR - https://www.scopus.com/pages/publications/85068483804

U2 - 10.1007/978-3-030-20671-0_24

DO - 10.1007/978-3-030-20671-0_24

M3 - Conference contribution

AN - SCOPUS:85068483804

SN - 9783030206703

T3 - IFIP Advances in Information and Communication Technology

SP - 351

EP - 363

BT - ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings

A2 - Dwivedi, Yogesh

A2 - Ayaburi, Emmanuel

A2 - Boateng, Richard

A2 - Effah, John

PB - Springer Science and Business Media, LLC

T2 - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019

Y2 - 21 June 2019 through 22 June 2019

ER -

Singh JP, Rana NP, Dwivedi YK. Rumour Veracity Estimation with Deep Learning for Twitter. In Dwivedi Y, Ayaburi E, Boateng R, Effah J, editors, ICT Unbounded, Social Impact of Bright ICT Adoption - IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Proceedings. Springer Science and Business Media, LLC. 2019. p. 351-363. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-20671-0_24

Rumour Veracity Estimation with Deep Learning for Twitter

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this