Journal of Visualization

, Volume 18, Issue 2, pp 221–235 | Cite as

SocialHelix: visual analysis of sentiment divergence in social media

  • Nan Cao
  • Lu Lu
  • Yu-Ru Lin
  • Fei Wang
  • Zhen Wen
Regular Paper


Social media allow people to express and promote different opinions, on which people’s sentiments to a subject often diverge when their opinions conflict. An intuitive visualization that unfolds the process of sentiment divergence from the rich and massive social media data will have far-reaching impact on various domains including social science, politics and economics. In this paper, we propose a visual analysis system, SocialHelix, to achieve this goal. SocialHelix is a novel visual design which enables the users to detect and trace topics and events occurring in social media, and to understand when and why divergences occurred and how they evolved among different social groups. We demonstrate the effectiveness and usefulness of SocialHelix by conducting in-depth case studies on tweets related to the national political debates.

Graphical Abstract


Information visualization Social media 



The authors wish to thank Prof. Huamin Qu from Hong Kong University of Science and Technology for his great support to this project.


  1. Amleshwaram AA, Reddy N, Yadav S, Gu G, Yang C (2013) In: International conference on communication systems and networks, pp 1–10Google Scholar
  2. Barbalet JM (2001) Emotion, social theory, and social structure: a macrosociological approach. Cambridge University Press, CambridgeGoogle Scholar
  3. Baur D, Seiffert F, Sedlmair M, Boring S (2010) IEEE Trans Vis Comput Graph 16(6):1119. doi: 10.1109/TVCG.2010.206
  4. Cao N, Lin YR, Sun X, Lazer D, Liu S, Qu H (2012) IEEE Trans Vis Comput Graph 18(12):2649Google Scholar
  5. Chen C, Ibekwe-Sanjuan F, SanJuan E, Weaver C (2006) In: IEEE VAST, pp 59–66Google Scholar
  6. Cui W, Liu S, Tan L, Shi C, Song Y, Gao ZJ, Tong X, Qu H (2011) IEEE Trans Vis Comput Graph 17(12):2412. doi: 10.1109/TVCG.2011.239
  7. Diakopoulos N, Naaman M, Yazdani T, Kivran-Swaine F (2011) In: Social media modeling and computing, pp 189–209Google Scholar
  8. Dörk M, Gruen D, Williamson C, Carpendale S (2010) IEEE Trans Vis Comput Graph 16(6):1129. doi: 10.1109/TVCG.2010.129
  9. Dou W, Wang X, Skau D, Ribarsky W, Zhou MX (2012) In: IEEE VAST, pp 93–102Google Scholar
  10. Evans D, Bratton S (2012) Social media marketing: an hour a day. Wiley, New YorkGoogle Scholar
  11. Fact-checking the first presidential debate (2012).
  12. Gamon M, Aue A, Corston-Oliver S, Ringger EK (2005) In: IDA, pp 121–132Google Scholar
  13. Gamon M, Basu S, Belenko D, Fisher D, Hurst M (2008) In: Proceedings of the international conference on weblogs and social media, pp 60–67Google Scholar
  14. Grafton Q, Knowles S, Owen PD (2001) Social divergence and economic performance. Tech. reportGoogle Scholar
  15. Gretarsson B, O’Donovan J, Bostandjiev S, Hall C, Höllerer T (2010) Comput Graph Forum 29(3):833. doi: 10.1111/j.1467-8659.2009.01679.x
  16. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall Inc, USAGoogle Scholar
  17. Kamps J, Mokken RJ, Marx M, de Rijke M (2004) In: Proceedings of the 4th international conference on language resources and evaluation, pp 1115–1118Google Scholar
  18. Kim T, Disalvo C (2012) Visualization of the public’s opinion on politically influential tweets. Tech. reportGoogle Scholar
  19. Kollock P, Smith M (1996) In: Computer-mediated communication: linguistic, social, and cross-cultural perspectives, pp 109–128Google Scholar
  20. Krstajić M, Bertini E, Keim D (2011) IEEE Trans Vis Comput Graph 17(12):2432. doi: 10.1109/TVCG.2011.179
  21. Kwak H, You J (2012) Visualizing a personal timeline by adding multiple social contexts. Tech. reportGoogle Scholar
  22. Lin YR, Keegan B, Margolin D, Lazer D (2014) PLoS One 9(5):e94093. doi: 10.1371/journal.pone.0094093
  23. Lin YR, Margolin D, Keegan B, Lazer D (2013) In: Proceedings of the 22nd international conference on world wide web, pp 737–748Google Scholar
  24. MacEachren AM, Jaiswal AR, Robinson AC, Pezanowski S, Savelyev A, Mitra P, Zhang X, Blanford J (2011) In: IEEE VAST, pp 181–190Google Scholar
  25. Munzner T (2009) IEEE Trans Vis Comput Graph 15(6):921Google Scholar
  26. Pak A, Paroubek P (2010) In: Proceedings of the 7th international conference on language resources and evaluation, pp 1320–1326Google Scholar
  27. Pang B, Lee L (2008) Found Trends Inf Retr 2(1–2):1Google Scholar
  28. Plutchik R (2001) Am Sci 89(4):344Google Scholar
  29. Pulse of the Nation (2012) US mood throughout the day inferred from Twitter.
  30. Rahim MA (2002) Int J Confl Manag 13(3):206Google Scholar
  31. Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011) In: Proceedings of the 20th international conference companion on world wide web, pp 249–252Google Scholar
  32. Schreck T, Keim DA (2013) Computer 46(5):68Google Scholar
  33. Shirky C (2011) Foreign Aff 90(1):28Google Scholar
  34. Tan S, Zhang J (2008) Expert Syst Appl 34(4):2622Google Scholar
  35. Thelwall M, Buckley K, Paltoglou G (2011) J Am Soc Inf Sci Technol 62(2):406Google Scholar
  36. Tweetstats (2012).
  37. Viégas F, Wattenberg M, Dave K (2004) In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 575–582Google Scholar
  38. Vinodhini G, Chandrasekaran R (2012) Int J Adv Res Comput Sci Softw Eng 2(6):282Google Scholar
  39. Vogel H (1979) Math Biosci 44(3–4):179Google Scholar
  40. Wefollow (2012).
  41. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San FranciscoGoogle Scholar
  42. Wongsuphasawat K, Gotz D (2012) IEEE Trans Vis Comput Graph 18(12):2659. doi: 10.1109/TVCG.2012.225
  43. Wu Y, Wei F, Liu S, Au N, Cui W, Zhou H, Qu H (2010) IEEE Trans Vis Comput Graph 16(6):1109Google Scholar
  44. Xia R, Zong C, Li S (2011) Inf Sci 181(6):1138Google Scholar

Copyright information

© The Visualization Society of Japan 2014

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA
  2. 2.Hong Kong University of Science and TechnologyClear Water BayHong Kong
  3. 3.School of Information ScienceUniversity of PittsburghPittsburghUSA
  4. 4.Department of Computer Science and EngineeringUniversity of ConnecticutStorrsUSA

Personalised recommendations