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Detection of rumor conversations in Twitter using graph convolutional networks

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

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Detection of rumor conversations in Twitter using graph convolutional networks. / Lotfi, Serveh; Mirzarezaee, Mitra; Hosseinzadeh, Mehdi et al.
Yn: Applied Intelligence, Cyfrol 51, Rhif 7, 01.07.2021, t. 4774–4787.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

Lotfi, S, Mirzarezaee, M, Hosseinzadeh, M & Seydi, V 2021, 'Detection of rumor conversations in Twitter using graph convolutional networks', Applied Intelligence, cyfrol. 51, rhif 7, tt. 4774–4787. https://doi.org/10.1007/s10489-020-02036-0

APA

Lotfi, S., Mirzarezaee, M., Hosseinzadeh, M., & Seydi, V. (2021). Detection of rumor conversations in Twitter using graph convolutional networks. Applied Intelligence, 51(7), 4774–4787. https://doi.org/10.1007/s10489-020-02036-0

CBE

Lotfi S, Mirzarezaee M, Hosseinzadeh M, Seydi V. 2021. Detection of rumor conversations in Twitter using graph convolutional networks. Applied Intelligence. 51(7):4774–4787. https://doi.org/10.1007/s10489-020-02036-0

MLA

Lotfi, Serveh et al. "Detection of rumor conversations in Twitter using graph convolutional networks". Applied Intelligence. 2021, 51(7). 4774–4787. https://doi.org/10.1007/s10489-020-02036-0

VancouverVancouver

Lotfi S, Mirzarezaee M, Hosseinzadeh M, Seydi V. Detection of rumor conversations in Twitter using graph convolutional networks. Applied Intelligence. 2021 Gor 1;51(7):4774–4787. Epub 2021 Ion 6. doi: 10.1007/s10489-020-02036-0

Author

Lotfi, Serveh ; Mirzarezaee, Mitra ; Hosseinzadeh, Mehdi et al. / Detection of rumor conversations in Twitter using graph convolutional networks. Yn: Applied Intelligence. 2021 ; Cyfrol 51, Rhif 7. tt. 4774–4787.

RIS

TY - JOUR

T1 - Detection of rumor conversations in Twitter using graph convolutional networks

AU - Lotfi, Serveh

AU - Mirzarezaee, Mitra

AU - Hosseinzadeh, Mehdi

AU - Seydi, Vahid

PY - 2021/7/1

Y1 - 2021/7/1

N2 - With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on Twitter. In the present paper, a model to detect rumor conversations is proposed using graph convolutional networks. A reply tree and user graph were extracted for each conversation. The reply trees were created according to the source tweet and the reply tweets. By modeling this graph on graph convolutional networks, structural information of the graph and the contents of conversation tweets were obtained. The user graphs were created based on the users participating in the conversation and the tweets exchanged among them. Information regarding the users and how they interacted in the conversations were obtained through modeling this graph on the graph convolutional networks. The outputs of the two above-mentioned modules were combined to detect the rumor. Experimental results on the public dataset show that the proposed method has a better performance than baseline methods.

AB - With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on Twitter. In the present paper, a model to detect rumor conversations is proposed using graph convolutional networks. A reply tree and user graph were extracted for each conversation. The reply trees were created according to the source tweet and the reply tweets. By modeling this graph on graph convolutional networks, structural information of the graph and the contents of conversation tweets were obtained. The user graphs were created based on the users participating in the conversation and the tweets exchanged among them. Information regarding the users and how they interacted in the conversations were obtained through modeling this graph on the graph convolutional networks. The outputs of the two above-mentioned modules were combined to detect the rumor. Experimental results on the public dataset show that the proposed method has a better performance than baseline methods.

U2 - 10.1007/s10489-020-02036-0

DO - 10.1007/s10489-020-02036-0

M3 - Article

VL - 51

SP - 4774

EP - 4787

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 7

ER -