Understanding the mechanism of social tie in the propagation process of social network with communication channel

Abstract

The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

Keywords

information propagation / social networks / mechanism of social tie / communication channel

References

[1] Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 137–146

[2] Tang J, Sun J M, Wang C, Yang Z. Social influence analysis in largescale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 807–816

[3] Romero D M, Meeder B, Kleinberg J. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 695–704

[4] Myers S A, Leskovec J. Clash of the contagions: cooperation and competition in information diffusion. In: Proceedings of the 12th IEEE International Conference on Data Mining. 2012, 539–548

[5] Abdullah N A, Nishioka D, Tanaka Y, Murayama Y. User’s action and decision making of retweet messages towards reducing misinformation spread during disaster. Journal of Information Processing, 2015, 23(1): 31–40

[6] Zhu K, Chen Z, Ying L. Catch’Em all: locating multiple diffusion sources in networks with partial observations. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017, 1676–1683

[7] Guille A, Hacid H, Favre C, Zighed D A. Information diffusion in online social networks: a survey. ACM Sigmod Record, 2013, 42(2): 17–28

[8] AlSumait L, Barbar D, Domeniconi C. On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 3–12

[9] Lu R, Yang Q. Trend analysis of news topics on twitter. International Journal of Machine Learning and Computing, 2012, 2(3): 327–332

[10] Hethcote H W. The mathematics of infectious diseases. SIAM Review, 2000, 42(4): 599–653

[11] Gomez R M, Leskovec J, Schölkopf B. Structure and dynamics of information pathways in online media. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013, 23–32

[12] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E, Makse H A. Identification of influential spreaders in complex networks. Nature Physics, 2010, 6(11): 888–893

[13] Pal A, Counts S. Identifying topical authorities in microblogs. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 45–54

[14] Goldenberg J, Libai B, Muller E. Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters, 2001, 12(3): 211–223

[15] Granovetter M. Threshold models of collective behavior. American Journal of Sociology, 1978, 83(6): 1420–1443

[16] Liu L, Tang J, Han J, Jiang M, Yang S. Mining topic-level influence in heterogeneous networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, 199–208

[17] Goyal A, Bonchi F, Lakshmanan L V. A data-based approach to social influence maximization. Proceedings of the VLDB Endowment, 2011, 5(1): 73–84

[18] Barbieri N, Bonchi F, Manco G. Topic-aware social influence propagation models. In: Proceedings of the 12th IEEE International Conference on Data Mining. 2012, 81–90

[19] Xiang B, Liu Q, Chen E H, Xiong H, Zheng Y, Yang Y. PageRank with priors: an influence propagation perspective. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2013, 2740–2746

[20] Hung H J, Shuai H H, Yang D N, Huang L H, Lee W C, Pei J, Chen M S. When social influence meets item inference. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 915–924

[21] Lei S, Maniu S, Mo L, Cheng R, Senellart P. Online influence maximization. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015, 645–654

[22] Feng S, Chen X, Cong G, Zeng Y F, Chee Y M, Xiang Y P. Influence maximization with novelty decay in social networks. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 37–43

[23] Yang Y, Tang J, Leung C W, Sun Y, Chen Q, Li J, Yang Q. RAIN: social role-aware information diffusion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 367–373

[24] Bourigault S, Lamprier S, Gallinari P. Representation learning for information diffusion through social networks: an embedded cascade model. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 573–582

[25] Zhao N, Zhang H, Wang M, Hong R, Chang C. Learning content-social influential features for influence analysis. International Journal of Multimedia Information Retrieval, 2016, 5(3): 137–149

[26] Saito K, Nakano R, Kimura M. Prediction of information diffusion probabilities for independent cascade model. In: Proceedings of International Conference on Knowledge-Based Intelligent Information and Engineering Systems. 2008, 67–75

[27] Netrapalli P, Sanghavi S. Learning the graph of epidemic cascades. ACM SIGMETRICS Performance Evaluation Review, 2012, 40(1): 211–222

[28] Goyal A, Bonchi F, Lakshmanan L V. Learning influence probabilities in social networks. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2008, 241–250

[29] Naveed N, Gottron T, Kunegis J, Alhadi A C. Bad news travel fast: a content-based analysis of interestingness on twitter. In: Proceedings of the 3rd International Web Science Conference. 2011, 8

[30] Petrovic S, Osborne M, Lavrenko V. RT to win! predicting message propagation in twitter. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. 2011, 586–589

[31] Firdaus S N, Ding C, Sadeghian A. Retweet prediction considering user’s difference as an author and retweeter. In: Proceeding of the 8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2016, 852–859

[32] Wang M, Zuo W L, Wang Y. A multidimensional nonnegative matrix factorization model for retweeting behavior prediction. Mathematical Problems in Engineering, 2015, 2015: 1–10

[33] Zhang K, Yun X, Liang J, Zhang X Y, Li C, Tian B. Retweeting behavior prediction using probabilistic matrix factorization. In: Proceedings of the 21st IEEE Symposium on Computers and Communication. 2016, 1185–1192

[34] Jiang B, Liang J, Sha Y, Li R, Liu W, Ma H, Wang L. Retweeting behavior prediction based on one-class collaborative filtering in social networks. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016, 977–980

[35] Pan R, Zhou Y, Cao B, Liu N, Lukose R, Scholz M, Yang Q. One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 502–511

[36] Ryan B, Gross N C. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, 1943, 8(1): 15

[37] Zhou W, Duan W, Piramuthu S. A social network matrix for implicit and explicit social network plates. Decision Support Systems, 2014, 68: 89–97

[38] Metaxas P T, Mustafaraj E, Wong K, Zeng L, O’Keefe M, Finn S. What do retweets indicate? Results from user survey and meta-review of research. In: Proceedings of the 9th International AAAI Conference on Web and Social Media. 2015, 658–661

[39] Cover T M, Thomas J A. Elements of Information Theory. Hoboken: John Wiley & Sons, 2012

[40] Zhang M L, Zhou Z H. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819–1837

[41] Gibaja E, Ventura S. A tutorial on multilabel learning. ACM Computing Surveys, 2015, 47(3): 52

[42] Darling W M. A theoretical and practical implementation tutorial on topic modeling and gibbs sampling. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011, 642–647

[43] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3(Jan): 993–1022

[44] Liu B, Lei Z. Mining Text Data (A Survey of Opinion Mining and Sentiment Analysis). New York: Springer US, 2012, 415–463

[45] Patel V, Gayatri P, Kiran B. A survey of opinion mining and sentiment analysis. International Journal of Computer Applications, 2015, 131(1): 24–27

[46] Macskassy S A, Provost F. A simple relational classifier. In: Proceedings of the 2nd Workshop on Multi-Relational Data Mining. 2003, 64–76

[47] Zhang J, Liu B, Tang J, Chen T, Li J. Social influence locality for modeling retweeting behaviors. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2761–2767

[48] Mahmud J, Zhou M X, Megiddo N, Nichols J, Drews C. Recommending targeted strangers from whom to solicit information on social media. In: Proceedings of the 18th International Conference on Intelligent User Interfaces. 2013, 37–48

[49] Yarkoni T. Personality in 100,000 words: a large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 2010, 44(3): 363–373

[50] Tausczik Y R, Pennebaker J W. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 2010, 29(1): 24–54

[51] Huang C L, Chung C K, Hui N, Lin Y C, Seih Y T, Lam B C, Chen W Q, Bond M H, Pennebaker J W. The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology, 2012, 54(2): 185–201

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature