Skip to main content

Advertisement

Log in

Diffusion of social conventions across polarized communities: an empirical study

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Social media environments often foster the formation of communities promoted by users’ tendencies toward homophily. These tendencies of connecting with similar users are solidified by the social media companies’ algorithmic and business practices, leading to polarized networks, where communities of different interests rarely interact. This paper investigates via simulations the adoption of a new convention promoted by a persistent minority in a network polarized into two communities. We perform experiments on two real-world networks and various synthetic networks with controlled properties. We discover that the position of the persistent minority has a greater impact on spreading new conventions than its relative size. We also show that although diffusion becomes harder as network polarization increases, a persistent minority can increase its effectiveness in promoting new conventions by targeting low-influence users from the opposite community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Adamic LA, Glance N (2005) The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd international workshop on link discovery. pp 36–43

  • Aisch G, Huang J, Kang C (2016) Dissecting the pizza gate conspiracy theories. www.nytimes.com/interactive/2016/12/10/business/media/pizzagate

  • Bail CA, Argyle LP, Brown TW, Bumpus JP, Chen H, Hunzaker MBF, Lee J, Mann M, Merhout F, Volfovsky A (2018) Exposure to opposing views on social media can increase political polarization. Proc Natl Acad Sci 115(37):9216–9221

    Article  Google Scholar 

  • Bakshy E, Messing S, Adamic LA (2015) Exposure to ideologically diverse news and opinion on Facebook. Science 348(6239):1130–1132

    Article  MathSciNet  Google Scholar 

  • Bozdag E, Gao Q, Houben GJ, Warnier M (2014) Does offline political segregation affect the filter bubble? An empirical analysis of information diversity for dutch and Turkish twitter users. Comput Hum Behav 41:405–415

    Article  Google Scholar 

  • Carley KM (2017) Social network analysis. Netanomics. http://netanomics.com/

  • Centola D (2010) The spread of behavior in an online social network experiment. Science 329–5996:1194–7

    Article  Google Scholar 

  • Centola D, Becker J, Brackbill D, Baronchelli A (2018) Experimental evidence for tipping points in social convention. Science 360(6393):1116–1119

    Article  Google Scholar 

  • Conover MD, Goncalves B, Ratkiewicz J, Flammini A, Menczer F (2011) Predicting the political alignment of Twitter users. In: 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. pp 192–199

  • Dahlerup D (2007) From a small to a large minority: women in scandinavian politics. Scand Polit Stud 11(4):275–298

    Article  Google Scholar 

  • El Tantawi M, Al-Ansari A, Alsubaie A, Fathy A, Aly MN, Mohamed A (2018) Reach of messages in a dental twitter network: cohort study examining user popularity, communication pattern, and network structure. J Med Internet Res 20:e10781

    Article  Google Scholar 

  • Gough A, Hunter RF, Ajao O, Jurek A, McKeown G, Hong J, Barrett E, Ferguson M, McElwee G, McCarthy M, Kee F (2017) Tweet for behavior change: using social media for the dissemination of public health messages. JMIR Public Health and Surveillance 3, New York

    Google Scholar 

  • Grey S (2006) Numbers and beyond: the relevance of critical mass in gender research. Politics Gend 2(4):492–502

    Google Scholar 

  • Guerra PH, Meira Jr W, Cardie C, Kleinberg R (2013) A measure of polarization on social media networks based on community boundaries. In: Proceedings of the 7th international conference on weblogs and social media, ICWSM. pp 215–224

  • Haim M, Graefe A, Brosius HB (2018) Burst of the filter bubble? Digital Journal 6(3):330–343

    Google Scholar 

  • Halu A, Zhao K, Baronchelli A, Bianconi G (2013) Connect and win: the role of social networks in political elections. EPL 102:16002

    Article  Google Scholar 

  • Handcock M, Hunter D, Butts C, Goodreau S, Morris M (2008) Statnet: software tools for the representation, visualization, analysis and simulation of network data. J Stat Softw 24:1548–7660

    Article  Google Scholar 

  • Hannak A, Sapiezynski P, Molavi Kakhki A, Krishnamurthy B, Lazer D, Mislove A, Wilson C (2013) Measuring personalization of web search. In: Proceedings of the 22nd international conference on World Wide Web. pp 527–538

  • Holland PW, Leinhardt S (1981) An exponential family of probability distributions for directed graphs. J Am Stat Assoc 76(373):33–50

    Article  MathSciNet  Google Scholar 

  • Joyce KE, Laurienti PJ, Burdette JH, Hayasaka S (2010) A new measure of centrality for brain networks. PLoS ONE 5:1–13

    Article  Google Scholar 

  • Kanter RM (1977) Some effects of proportions on group life: skewed sex ratios and responses to token women. Am J Sociol 82(5):965–990

    Article  Google Scholar 

  • Karypis G, Kumar V (1998) METIS: a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices

  • King I, Lyu M, Hao M (2010) Introduction to social recommendation. In: Proceedings of the 19th international conference on World Wide Web

  • Livne A, Simmons MP, Adar E, Adamic LA (2011) The party is over here: structure and content in the 2010 election. In: ICWSM

  • McNamee R (2019) Zucked: waking up to the Facebook catastrophe. Penguin Press, London

    Google Scholar 

  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415–444

    Article  Google Scholar 

  • Nair S, Adriana I, John S (2019) Promoting social conventions across polarized networks: an empirical study. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM ’19

  • Nardini C, Kozma B, Barrat A (2008) Who’s talking first? Consensus or lack thereof in co-evolving opinion formation models. Phys Rev Lett 100:158701

    Article  Google Scholar 

  • O’Connor B, Balasubramanyan RR, Routledge BA, Smith N (2010) From tweets to polls: linking text sentiment to public opinion time series. In: International AAAI conference on weblogs and social media

  • Pal S, Kundu S, Murthy C (2014) Centrality measures, upper bound, and influence maximization in large scale directed social networks. Fundam Inf 130:317–342

    MathSciNet  Google Scholar 

  • Pariser E (2011) The Filter bubble: what the internet is hiding from you. Penguin Books, London

    Google Scholar 

  • Rader E, Gray R (2015) Understanding user beliefs about algorithmic curation in the facebook news feed. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, CHI ’15. pp 173–182

  • Silverman C (2016) Here are 50 of the biggest fake news hits on facebook from 2016. www.buzzfeednews.com/article/craigsilverman/top-fake-news-of-2016

  • Singh P, Sreenivasan S, Szymanski BK, Korniss G (2012) Accelerating consensus on co-evolving networks: the effect of committed individuals. Phys Rev E Stat Nonlinear Soft Matter Phys 85(4):046104

    Article  Google Scholar 

  • Syria MJ Whitehelmets: rescue workers protect aleppo, Times. https://time.com/syria-white-helmets/

  • Tumasjan A, Oliver ST, Sandner P, Welpe I (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Fourth international AAAI conference on weblogs and social media

  • Xie J, Sreenivasan S, Korniss G, Zhang W, Lim C, Szymanski B (2011) Social consensus through the influence of committed minorities. Phys Rev E84:011130

    Google Scholar 

  • Young HP (2011) The dynamics of social innovation. Proc Natl Acad Sci 108(Supplement 4):21285–21291

    Article  Google Scholar 

  • Zhou X, Xu Y, Li Y, Josang A, Cox C (2012) The state-of-the-art in personalized recommender systems for social networking. Artif Intell Rev 37(2):119–132

    Article  Google Scholar 

Download references

Acknowledgements

Supported by the Office of Naval Research Grant N00014-18-1-2128, the US National Science Foundation Grant IIS 1546453 and the DARPA SocialSim Program and the Air Force Research Laboratory under contract FA8650-18-C-7825.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sreeja Nair.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nair, S., Ng, K.W., Iamnitchi, A. et al. Diffusion of social conventions across polarized communities: an empirical study. Soc. Netw. Anal. Min. 11, 17 (2021). https://doi.org/10.1007/s13278-021-00726-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-021-00726-2

Keywords

Navigation