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Probabilistic reasoning system for social influence analysis in online social networks

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Abstract

People interact with other people in their daily life, either for work or for personal reasons. These interactions are often complex. Thus, interactions that an individual has with other individuals, to some extent, influence the decisions they make. There have been many efforts to uncover, explore, and measure the concept of social influence. Thus, modeling influence is an open and challenging problem where most evaluation models focus on online social networks. However, they fail to characterize some properties of social influence. To address the limitations of the previous approaches, we propose a novel Probabilistic Reasoning system for social INfluence analysis (PRIN) to examine the social influence process and elucidate the factors that affect it in an attempt to explain this phenomenon. In this paper, we present a model that quantitatively measures social influence in online social networks. Experiments on a real social network such as Twitter demonstrate that the proposed model significantly outperforms traditional feature engineering-based approaches. This suggests the effectiveness of this novel model when modeling and predicting social influence.

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Notes

  1. https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products

  2. http://www.twitter.com, a microblogging system.

  3. http://www.digg.com, a social news sharing and voting website.

  4. https://www.reddit.com/r/socialmedia/, a social sharing website.

References

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  Google Scholar 

  • Bi B, Tian Y, Sismanis Y, Balmin A, Cho J (2014) Scalable topic-specific influence analysis on microblogs. In: Proceedings of the 7th ACM international conference on Web search and data mining, ACM, pp 513–522

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    MATH  Google Scholar 

  • Biswas A, Biswas B (2017) Community-based link prediction. Multimedia Tools Appl 76(18):18619–18639

    Article  Google Scholar 

  • Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362

    Article  Google Scholar 

  • Brown PF, Pietra VJD, Mercer RL, Pietra SAD, Lai JC (1992) An estimate of an upper bound for the entropy of English. Comput Linguist 18(1):31–40. http://dl.acm.org/citation.cfm?id=146680.146685

  • Cai H, Zheng VW, Zhu F, Chang KCC, Huang Z (2017) From community detection to community profiling. Proc VLDB Endow 10(7):817–828

    Article  Google Scholar 

  • Chang J, Gerrish S, Wang C, Boyd-Graber JL, Blei DM (2009) Reading tea leaves: How humans interpret topic models. In: Advances in neural information processing systems, pp 288–296

  • Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 160–168

  • De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980

    Article  Google Scholar 

  • Diao Q, Jiang J, Zhu F, Lim EP (2012) Finding bursty topics from microblogs. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers-volume 1, Association for Computational Linguistics, pp 536–544

  • Doucet A, De Freitas N, Murphy K, Russell S (2000) Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: Proceedings of the sixteenth conference on uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., pp 176–183

  • Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. In: ACM SIGCOMM computer communication review, ACM, vol 29, pp 251–262

  • Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9(Aug):1871–1874

    MATH  Google Scholar 

  • Fei H, Jiang R, Yang Y, Luo B, Huan J (2011) Content based social behavior prediction: a multi-task learning approach. In: Proceedings of the 20th ACM international conference on Information and knowledge management, ACM, pp 995–1000

  • Franks H, Griffiths N, Anand SS (2013) Learning influence in complex social networks. In: Proceedings of the 2013 international conference on autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems, pp 447–454

  • Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  • Goldberg Y (2017) Neural network methods for natural language processing. Synth Lect Hum Lang Technol 10(1):1–309

    Article  Google Scholar 

  • Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining, ACM, pp 241–250

  • Granovetter MS (1977) The strength of weak ties. In: Social networks, Elsevier, pp 347–367

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

    Article  Google Scholar 

  • Han M, Li Y (2018) Influence analysis: a survey of the state-of-the-art. Math Found Comput 1(3):201–253

    Article  Google Scholar 

  • Hu Z, Wang C, Yao J, Xing E, Yin H, Cui B (2013) Community specific temporal topic discovery from social media. ArXiv preprint arXiv:13120860

  • Hu Z, Yao J, Cui B, Xing E (2015) Community level diffusion extraction. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, ACM, pp 1555–1569

  • Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. ArXiv preprint arXiv:08121045

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 137–146

  • Koller D, Friedman N, Bach F (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge

    Google Scholar 

  • Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods. Information 8(4):118

    Article  Google Scholar 

  • Liu L, Tang J, Han J, Yang S (2012) Learning influence from heterogeneous social networks. Data Min Knowl Disc 25(3):511–544

    Article  MathSciNet  Google Scholar 

  • March JG (1955) An introduction to the theory and measurement of influence. Am Polit Sci Rev 49(2):431–451

    Article  Google Scholar 

  • Miller JJ (2013) Graph database applications and concepts with Neo4j. In: Proceedings of the southern association for information systems conference, Atlanta, GA, USA, vol 2324

  • Nallapati RM, Ahmed A, Xing EP, Cohen WW (2008) Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 542–550

  • Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  Google Scholar 

  • Pálovics R, Benczúr AA, Kocsis L, Kiss T, Frigó E (2014) Exploiting temporal influence in online recommendation. In: Proceedings of the 8th ACM conference on recommender systems, ACM, pp 273–280

  • Pezzoni F, An J, Passarella A, Crowcroft J, Conti M (2013) Why do i retweet it? An information propagation model for microblogs. In: International conference on social informatics, Springer, pp 360–369

  • Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018) DeepInf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 2110–2119

  • Rashotte L (2007) Social influence. The Blackwell Encyclopedia of Sociology, pp 4426–4428

  • Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: International conference on knowledge-based and intelligent information and engineering systems, Springer, pp 67–75

  • Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268

    Article  Google Scholar 

  • Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 807–816

  • Travers J, Milgram S (1967) The small world problem. Phychol Today 1(1):61–67

    Google Scholar 

  • Vega L, Mendez-Vazquez A (2016) Dynamic neural networks for text classification. In: 2016 international conference on computational intelligence and applications (ICCIA), IEEE, pp 6–11

  • Wen Z, Lin CY (2010) On the quality of inferring interests from social neighbors. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 373–382

  • Weng J, Lim EP, Jiang J, He Q (2010) TwitterRank: finding topic-sensitive influential Twitterers. In: Proceedings of the third ACM international conference on Web search and data mining, ACM, pp 261–270

  • Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of the 19th international conference on World wide web, ACM, pp 981–990

  • Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv (CSUR) 45(4):43

    Article  Google Scholar 

  • Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: 2010 IEEE international conference on data mining, IEEE, pp 599–608

  • Yang Z, Guo J, Cai K, Tang J, Li J, Zhang L, Su Z (2010) Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM international conference on information and knowledge management, ACM, pp 1633–1636

  • Zhang J, Liu B, Tang J, Chen T, Li J (2013) Social influence locality for modeling retweeting behaviors. In: Twenty-third international joint conference on artificial intelligence

  • Zhang J, Tang J, Li J, Liu Y, Xing C (2015) Who influenced you? Predicting retweet via social influence locality. ACM Trans Knowl Discov Data (TKDD) 9(3):25

    Google Scholar 

  • Zhu Y, Yan X, Getoor L, Moore C (2013) Scalable text and link analysis with mixed-topic link models. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 473–481

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Acknowledgements

The authors would like to thank CONACYT for the financial support.

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Authors and Affiliations

  1. Research Center for Advanced Study from the National Polytechnic Institute, Zapopan, Mexico

    Lea Vega & Andres Mendez-Vazquez

  2. Technological and Higher Education Institute of Monterrey, Monterrey, Mexico

    Armando López-Cuevas

Authors
  1. Lea Vega
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  2. Andres Mendez-Vazquez
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  3. Armando López-Cuevas
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Correspondence to Lea Vega.

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Vega, L., Mendez-Vazquez, A. & López-Cuevas, A. Probabilistic reasoning system for social influence analysis in online social networks. Soc. Netw. Anal. Min. 11, 1 (2021). https://doi.org/10.1007/s13278-020-00705-z

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