Learning influence probabilities and modelling influence diffusion in twitter

Zizhu Zhang; Weiliang Zhao; Jian Yang; Cecile Paris; Surya Nepal

doi:10.1145/3308560.3316701

Learning influence probabilities and modelling influence diffusion in twitter

Zizhu Zhang, Weiliang Zhao, Jian Yang, Cecile Paris, Surya Nepal

School of Computing

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

18 Citations (Scopus)

Abstract

Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people's influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.

Original language	English
Title of host publication	WWW '19 Companion Proceedings of the 2019 World Wide Web Conference
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	1087-1094
Number of pages	8
ISBN (Electronic)	9781450366755
DOIs	https://doi.org/10.1145/3308560.3316701
Publication status	Published - 13 May 2019
Event	2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019

Conference

Conference	2019 World Wide Web Conference, WWW 2019
Country/Territory	United States
City	San Francisco
Period	13/05/19 → 17/05/19

Keywords

Depth decay
Influence diffusion
Influence probability
Time decay
Twitter

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10.1145/3308560.3316701

Cite this

@inproceedings{fed5c401bc4944b18903f72923dc470c,

title = "Learning influence probabilities and modelling influence diffusion in twitter",

abstract = "Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people's influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.",

keywords = "Depth decay, Influence diffusion, Influence probability, Time decay, Twitter",

author = "Zizhu Zhang and Weiliang Zhao and Jian Yang and Cecile Paris and Surya Nepal",

year = "2019",

month = may,

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doi = "10.1145/3308560.3316701",

language = "English",

pages = "1087--1094",

booktitle = "WWW '19 Companion Proceedings of the 2019 World Wide Web Conference",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",

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Zhang, Z, Zhao, W , Yang, J , Paris, C & Nepal, S 2019, Learning influence probabilities and modelling influence diffusion in twitter. in WWW '19 Companion Proceedings of the 2019 World Wide Web Conference. Association for Computing Machinery (ACM), New York, pp. 1087-1094, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 13/05/19. https://doi.org/10.1145/3308560.3316701

Learning influence probabilities and modelling influence diffusion in twitter. / Zhang, Zizhu; Zhao, Weiliang ; Yang, Jian et al.
WWW '19 Companion Proceedings of the 2019 World Wide Web Conference. New York: Association for Computing Machinery (ACM), 2019. p. 1087-1094.

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

TY - GEN

T1 - Learning influence probabilities and modelling influence diffusion in twitter

AU - Zhang, Zizhu

AU - Zhao, Weiliang

AU - Yang, Jian

AU - Paris, Cecile

AU - Nepal, Surya

PY - 2019/5/13

Y1 - 2019/5/13

N2 - Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people's influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.

AB - Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people's influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.

KW - Depth decay

KW - Influence diffusion

KW - Influence probability

KW - Time decay

KW - Twitter

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U2 - 10.1145/3308560.3316701

DO - 10.1145/3308560.3316701

M3 - Conference proceeding contribution

AN - SCOPUS:85066912690

SP - 1087

EP - 1094

BT - WWW '19 Companion Proceedings of the 2019 World Wide Web Conference

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - 2019 World Wide Web Conference, WWW 2019

Y2 - 13 May 2019 through 17 May 2019

ER -

Learning influence probabilities and modelling influence diffusion in twitter

Abstract

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Keywords

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