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Published in: Social Network Analysis and Mining 1/2021

01-12-2021 | Original Article

Probabilistic reasoning system for social influence analysis in online social networks

Authors: Lea Vega, Andres Mendez-Vazquez, Armando López-Cuevas

Published in: Social Network Analysis and Mining | Issue 1/2021

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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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Footnotes
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.
 
Literature
go back to reference 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 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
go back to reference Bishop CM (2006) Pattern recognition and machine learning. Springer, BerlinMATH Bishop CM (2006) Pattern recognition and machine learning. Springer, BerlinMATH
go back to reference Biswas A, Biswas B (2017) Community-based link prediction. Multimedia Tools Appl 76(18):18619–18639CrossRef Biswas A, Biswas B (2017) Community-based link prediction. Multimedia Tools Appl 76(18):18619–18639CrossRef
go back to reference Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362CrossRef Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362CrossRef
go back to reference Cai H, Zheng VW, Zhu F, Chang KCC, Huang Z (2017) From community detection to community profiling. Proc VLDB Endow 10(7):817–828CrossRef Cai H, Zheng VW, Zhu F, Chang KCC, Huang Z (2017) From community detection to community profiling. Proc VLDB Endow 10(7):817–828CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980CrossRef De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9(Aug):1871–1874MATH Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9(Aug):1871–1874MATH
go back to reference 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 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
go back to reference 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 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
go back to reference Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239CrossRef Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239CrossRef
go back to reference Goldberg Y (2017) Neural network methods for natural language processing. Synth Lect Hum Lang Technol 10(1):1–309CrossRef Goldberg Y (2017) Neural network methods for natural language processing. Synth Lect Hum Lang Technol 10(1):1–309CrossRef
go back to reference 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 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
go back to reference Granovetter MS (1977) The strength of weak ties. In: Social networks, Elsevier, pp 347–367 Granovetter MS (1977) The strength of weak ties. In: Social networks, Elsevier, pp 347–367
go back to reference Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM Sigmod Record 42(2):17–28CrossRef Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM Sigmod Record 42(2):17–28CrossRef
go back to reference Han M, Li Y (2018) Influence analysis: a survey of the state-of-the-art. Math Found Comput 1(3):201–253CrossRef Han M, Li Y (2018) Influence analysis: a survey of the state-of-the-art. Math Found Comput 1(3):201–253CrossRef
go back to reference 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, Wang C, Yao J, Xing E, Yin H, Cui B (2013) Community specific temporal topic discovery from social media. ArXiv preprint arXiv:​13120860
go back to reference 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 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
go back to reference Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. ArXiv preprint arXiv:08121045 Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. ArXiv preprint arXiv:​08121045
go back to reference 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 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
go back to reference Koller D, Friedman N, Bach F (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge Koller D, Friedman N, Bach F (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge
go back to reference Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods. Information 8(4):118CrossRef Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods. Information 8(4):118CrossRef
go back to reference Liu L, Tang J, Han J, Yang S (2012) Learning influence from heterogeneous social networks. Data Min Knowl Disc 25(3):511–544MathSciNetCrossRef Liu L, Tang J, Han J, Yang S (2012) Learning influence from heterogeneous social networks. Data Min Knowl Disc 25(3):511–544MathSciNetCrossRef
go back to reference March JG (1955) An introduction to the theory and measurement of influence. Am Polit Sci Rev 49(2):431–451CrossRef March JG (1955) An introduction to the theory and measurement of influence. Am Polit Sci Rev 49(2):431–451CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference Rashotte L (2007) Social influence. The Blackwell Encyclopedia of Sociology, pp 4426–4428 Rashotte L (2007) Social influence. The Blackwell Encyclopedia of Sociology, pp 4426–4428
go back to reference 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 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
go back to reference 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 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
go back to reference Travers J, Milgram S (1967) The small world problem. Phychol Today 1(1):61–67 Travers J, Milgram S (1967) The small world problem. Phychol Today 1(1):61–67
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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):43CrossRef 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):43CrossRef
go back to reference Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: 2010 IEEE international conference on data mining, IEEE, pp 599–608 Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: 2010 IEEE international conference on data mining, IEEE, pp 599–608
go back to reference 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 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
go back to reference 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, 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
go back to reference 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 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
go back to reference 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 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
Metadata
Title
Probabilistic reasoning system for social influence analysis in online social networks
Authors
Lea Vega
Andres Mendez-Vazquez
Armando López-Cuevas
Publication date
01-12-2021
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2021
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00705-z

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