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Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1480–1505 | Cite as

Measuring and moderating opinion polarization in social networks

  • Antonis Matakos
  • Evimaria Terzi
  • Panayiotis Tsaparas
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

The polarization of society over controversial social issues has been the subject of study in social sciences for decades (Isenberg in J Personal Soc Psychol 50(6):1141–1151, 1986, Sunstein in J Polit Philos 10(2):175–195, 2002). The widespread usage of online social networks and social media, and the tendency of people to connect and interact with like-minded individuals has only intensified the phenomenon of polarization (Bakshy et al. in Science 348(6239):1130–1132, 2015). In this paper, we consider the problem of measuring and reducing polarization of opinions in a social network. Using a standard opinion formation model (Friedkin and Johnsen in J Math Soc 15(3–4):193–206, 1990), we define the polarization index, which, given a network and the opinions of the individuals in the network, it quantifies the polarization observed in the network. Our measure captures the tendency of opinions to concentrate in network communities, creating echo-chambers. Given this numeric measure of polarization, we then consider the problem of reducing polarization in the network by convincing individuals (e.g., through education, exposure to diverse viewpoints, or incentives) to adopt a more neutral stand towards controversial issues. We formally define the ModerateInternal and ModerateExpressed problems, and we prove that both our problems are NP-hard. By exploiting the linear-algebraic characteristics of the opinion formation model we design polynomial-time algorithms for both problems. Our experiments with real-world datasets demonstrate the validity of our metric, and the efficiency and the effectiveness of our algorithms in practice.

Keywords

Polarization Social networks Opinion formation Moderation 

Notes

Acknowledgements

This work was supported by the Marie Curie Reintegration Grant projects titled JMUGCS which has received research funding from the European Union, and the National Science Foundation grants: IIS 1320542, IIS 1421759, CAREER 1253393, as well as a gift from Microsoft. We would also like to thank Evaggelia Pitoura for useful comments and discussions on early drafts of the paper.

References

  1. Adamic LA, Glance N (2005) The political blogosphere and the 2004 u.s. election: Divided they blog. In: International workshop on link discovery, LinkKDDGoogle Scholar
  2. Akoglu L (2014) Quantifying political polarity based on bipartite opinion networks. In: International conference on weblogs and social media, ICWSMGoogle Scholar
  3. Amelkin V, Singh AK, Bogdanov P (2015) A distance measure for the analysis of polar opinion dynamics in social networks. arXiv:1510.05058
  4. Bakshy E, Messing S, Adamic L (2015) Exposure to ideologically diverse news and opinion on Facebook. Science 348(6239):1130–1132MathSciNetCrossRefzbMATHGoogle Scholar
  5. Bessi A, Zollo F, Vicario MD, Puliga M, Scala A, Caldarelli G, Uzzi B, Quattrociocchi W (2016) Users polarization on Facebook and Youtube. PLoS ONE 11(8):e0159641CrossRefGoogle Scholar
  6. Bindel D, Kleinberg JM, Oren S (2015) How bad is forming your own opinion? Games Econ Behav 92:248–265MathSciNetCrossRefzbMATHGoogle Scholar
  7. Cambria E, Poria S, Bisio F, Bajpai R, Chaturvedi I (2015) The CLSA model: a novel framework for concept-level sentiment analysis. Springer International Publishing, Cham. doi: 10.1007/978-3-319-18117-2_1 Google Scholar
  8. Cambria E, Poria S, Bajpai R, Schuller BW (2016) SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: 26th International conference on computational linguistics (COLING 2016), Proceedings of the conference: Technical Papers, Osaka, Japan, December 11–16, 2016, pp. 2666–2677Google Scholar
  9. Chen T, Xu R, He Y, Xia Y, Wang X (2016) Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Comp Int Mag 11(3):34–44. doi: 10.1109/MCI.2016.2572539 CrossRefGoogle Scholar
  10. Conover M, Ratkiewicz J, Francisco MR, Gonçalves B, Menczer F, Flammini A (2011) Political polarization on Twitter. In: International conference on weblogs and social media ICWSMGoogle Scholar
  11. Dandekar P, Goel A, Lee DT (2013) Biased assimilation, homophily, and the dynamics of polarization. Proc Natl Acad Sci 110(15):5791–5796MathSciNetCrossRefzbMATHGoogle Scholar
  12. Davis G, Mallat S, Zhang Z (1994) Adaptive time-frequency decompositions with matching pursuits. Opt Eng 33(7):2183–2191Google Scholar
  13. Del Vicario M, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W (2017) Modeling confirmation bias and polarization. Sci Rep 7:40391. doi: 10.1038/srep40391 CrossRefGoogle Scholar
  14. Feige U (2003) Vertex cover is hardest to approximate on regular graphs. Technical report MCS03-15 of the Weizmann InstituteGoogle Scholar
  15. Friedkin NE, Johnsen E (1990) Social influence and opinions. J Math Soc 15(3–4):193–206CrossRefzbMATHGoogle Scholar
  16. Garimella K, Morales GDF, Gionis A, Mathioudakis M (2016) Quantifying controversy in social media. In: ACM international conference on web search and data mining, WSDM, pp 33–42Google Scholar
  17. Garimella VRK, Morales GDF, Gionis A, Mathioudakis M (2017) Reducing controversy by connecting opposing views. In: ACM WISDOM international conference on web search and data miningGoogle Scholar
  18. Garrett RK (2009) Echo chambers online? Politically motivated selective exposure among internet news users1. J Comput Mediat Commun 14(2):265–285. doi: 10.1111/j.1083-6101.2009.01440.x MathSciNetCrossRefGoogle Scholar
  19. Gionis A, Terzi E, Tsaparas P (2013) Opinion maximization in social networks. In: SIAM international conference on data mining, pp 387–395Google Scholar
  20. Guerra PHC, Jr, WM, Cardie C, Kleinberg R (2013) A measure of polarization on social media networks based on community boundaries. In: International conference on weblogs and social media, ICWSMGoogle Scholar
  21. Hager WW (1989) Updating the inverse of a matrix. SIAM Rev 31(2):221–239MathSciNetCrossRefzbMATHGoogle Scholar
  22. Isenberg DJ (1986) Group polarization: a critical review and meta-analysis. J Personal Soc Psychol 50(6):1141–1151CrossRefGoogle Scholar
  23. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 137–146Google Scholar
  24. Lappas T, Crovella M, Terzi E (2012) Selecting a characteristic set of reviews. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 832–840Google Scholar
  25. Lawrence P, Sergey B, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. Technical report, Stanford UniversityGoogle Scholar
  26. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167MathSciNetCrossRefGoogle Scholar
  27. Mallat S (2008) A wavelet tour of signal processing, third edition: the sparse way, 3rd edn. Academic Press, CambridgezbMATHGoogle Scholar
  28. Munson SA, Lee SY, Resnick P (2013) Encouraging reading of diverse political viewpoints with a browser widget. In: International conference on weblogs and social media, ICWSMGoogle Scholar
  29. Munson SA, Resnick P (2010) Presenting diverse political opinions: how and how much. In: International conference on human factors in computing systems, CHI, pp 1457–1466Google Scholar
  30. Natarajan BK (1995) Sparse approximate solutions to linear systems. SIAM J Comput 24(2):227–234MathSciNetCrossRefzbMATHGoogle Scholar
  31. Pariser E (2011) The filter bubble: what the internet is hiding from you. The Penguin GroupGoogle Scholar
  32. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108(C):42–49. doi: 10.1016/j.knosys.2016.06.009 CrossRefGoogle Scholar
  33. Sunstein CR (2002) The law of group polarization. J Polit Philos 10(2):175–195CrossRefGoogle Scholar
  34. Vicario MD, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W (2016) Modeling confirmation bias and polarization. arXiv:1607.00022
  35. Vydiswaran V, Zhai C, Roth D, Pirolli P (2015) Overcoming bias to learn about controversial topics. J Assoc Inf Sci Technol 66(8):1655–1672CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Department of Computer ScienceBoston UniversityBostonUSA

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