International Workshop on Databases in Networked Information Systems

DNIS 2014: Databases in Networked Information Systems pp 1-16 | Cite as

Big Data in Online Social Networks: User Interaction Analysis to Model User Behavior in Social Networks

  • Divyakant Agrawal
  • Ceren Budak
  • Amr El Abbadi
  • Theodore Georgiou
  • Xifeng Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8381)

Abstract

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a multiple contexts. Because many social interactions currently take place in online networks, social scientists have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, these investigations required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of big data can allow social scientists to study social interactions on a scale and at a level of detail that has never before been possible. Our goal is to evaluate the value of big data in various social applications and build a framework that models the cost/utility of data. By considering important problems such as Trend Analysis, Opinion Change and User Behavior Analysis during major events in online social networks, we demonstrate the significance of this problem. Furthermore, in each case we present scalable techniques and algorithms that can be used in an online manner. Finally, we propose the big data value evaluation framework that weighs in the cost as well as the value of data to determine capacity modeling in the context of data acquisition.

Keywords

Social Networks Big Data Social Analytics Data Streams Complex Networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proc. 29th Int. Conf. on Very Large Data Bases, pp. 81–92. VLDB Endowment (2003)Google Scholar
  3. 3.
    Aggarwal, C.C., Yu, P.S.: Online analysis of community evolution in data streams. In: Proc. SIAM International Data Mining Conference (2005)Google Scholar
  4. 4.
    Allan, J. (ed.): Topic detection and tracking: event-based information organization. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  5. 5.
    Alon, N., Yuster, R., Zwick, U.: Finding and counting given length cycles. Algorithmica 1717, 209–223 (1997)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Bar-Yossef, Z., Kumar, R., Sivakumar, D.: Reductions in streaming algorithms, with an application to counting triangles in graphs. In: SODA 2002, pp. 623–632 (2002)Google Scholar
  7. 7.
    Becchetti, L., Boldi, P., Castillo, C., Gionis, A.: Efficient semi-streaming algorithms for local triangle counting in massive graphs. In: KDD 2008, pp. 16–24 (2008)Google Scholar
  8. 8.
    Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 693–703. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Chen, B.: Topic oriented evolution and sentiment analysis. Ph.D. Dissertation, Penn State University (2011)Google Scholar
  10. 10.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD 2009, pp. 199–208 (2009)Google Scholar
  11. 11.
    Chierichetti, F., Kleinberg, J., Panconesi, A.: How to schedule a cascade in an arbitrary graph. In: EC 2012, pp. 355–368 (2012)Google Scholar
  12. 12.
    Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. Proc. VLDB Endow. 1(2), 1530–1541 (2008)CrossRefGoogle Scholar
  13. 13.
    Cormode, G., Muthukrishnan, S.: What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically. TODS 2005 30(1), 249–278 (2005)MathSciNetGoogle Scholar
  14. 14.
  15. 15.
    Friedkin, N.E.: The attitude-behavior linkage in behavioral cascades. Social Psychology Quarterly, 73–196 (2010)Google Scholar
  16. 16.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: ICML 2011 (2011)Google Scholar
  17. 17.
    Hartline, J., Mirrokni, V., Sundararajan, M.: Optimal marketing strategies over social networks. In: WWW 2008, pp. 189–198 (2008)Google Scholar
  18. 18.
    Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., Tsioutsiouliklis, K.: Discovering geographical topics in the twitter stream. In: WWW 2012, pp. 769–778 (2012)Google Scholar
  19. 19.
    Horrigan, J., Rainie, L.: When facing a tough decision, 60 million americans now seek the internet’s help: The internet’s growing role in life’s major moments (2006), http://pewresearch.org/obdeck/?ObDeckID=19 (retrieved October 13, 2006)
  20. 20.
    Howe, J.: The rise of crowdsourcing. North 14(14), 1–5 (2006)Google Scholar
  21. 21.
    Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management 6(3/4), 248 (2009)CrossRefGoogle Scholar
  22. 22.
  23. 23.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: HLT 2011, pp. 151–160 (2011)Google Scholar
  24. 24.
    Jin, C., Qian, W., Sha, C., Yu, J.X., Zhou, A.: Dynamically maintaining frequent items over a data stream. In: CIKM 2003, pp. 287–294. ACM (2003)Google Scholar
  25. 25.
    Katz, I., Tunstrom, K., Ioannou, C., Huepe, C., Couzin, I.: Inferring the structure and dynamics of interactions in schooling fish. In: PNAS 2011, pp. 18720–18725 (2011)Google Scholar
  26. 26.
    Kempe, D., Kleinber, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003, pp. 137–146 (2003)Google Scholar
  27. 27.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  28. 28.
    Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: Proc. 30th Int. Conf. on Very Large Data Bases, pp. 180–191. VLDB Endowment (2004)Google Scholar
  29. 29.
    Kimura, M., Saito, K., Nakano, R., Motoda, H.: Extracting influential nodes on a social network for information diffusion. Data Mining and Knowledge Discovery 20, 70–97 (2010)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Kimura, M., Saito, K., Ohara, K., Motoda, H.: Learning to predict opinion share in social networks. In: AAAI 2010, pp. 1364–1370 (2010)Google Scholar
  31. 31.
    Kittur, A., Kraut, R.E.: Harnessing the wisdom of crowds in wikipedia: quality through coordination. In: Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, CSCW 2008, pp. 37–46. ACM, New York (2008)CrossRefGoogle Scholar
  32. 32.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the First Workshop on Online Social Networks, WOSN 2008, pp. 19–24. ACM (2008)Google Scholar
  33. 33.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media. In: WWW 2010, pp. 591–600 (2010)Google Scholar
  34. 34.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 497–506 (2009)Google Scholar
  35. 35.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: KDD 2009, pp. 497–506 (2009)Google Scholar
  36. 36.
    Libert, B., Spector, J.: We are smarter than me: how to unleash the power of crowds in your business, 1st edn. Wharton School Publishing (2007)Google Scholar
  37. 37.
    Lin, Y.R., Margolin, D., Keegan, B., Lazer, D.: Voices of Victory: A Computational Focus Group Framework for Tracking Opinion Shift in Real Time. In: WWW 2013, pp. 737–747 (2013)Google Scholar
  38. 38.
    MacEachren, A.M., Robinson, A.C., Jaiswal, A., Pezanov, S., Savelyev, A., Blanford, J., Mitra, P.: Geo-Twitter analytics: Application in crisis management. In: 25th International Cartographic Conference (July 2011)Google Scholar
  39. 39.
    Macropol, K., Singh, A.K.: Content-based modeling and prediction of information dissemination. In: ASONAM 2011, pp. 21–28 (2011)Google Scholar
  40. 40.
    Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: VLDB 2002, pp. 346–357 (2002)Google Scholar
  41. 41.
    Mehta, R., Mehta, D., Chheda, D., Shah, C., Chawan, P.: Sentiment analysis and influence tracking using twitter. International Journal of Advanced Research in Computer Science and Electronics Engineering 1, 72–79 (2012)Google Scholar
  42. 42.
    Melville, W.G.P., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD 2009, pp. 1275–1284 (2009)Google Scholar
  43. 43.
    Metwally, A., Agrawal, D., El Abbadi, A.: An integrated efficient solution for computing frequent and top-k elements in data streams. ACM Trans. Database Syst. 31(3), 1095–1133 (2006)CrossRefGoogle Scholar
  44. 44.
    Metwally, A., Emekçi, F., Agrawal, D., El Abbadi, A.: Sleuth: Single-publisher attack detection using correlation hunting. Proc. VLDB Endow. 1(2), 1217–1228 (2008)CrossRefGoogle Scholar
  45. 45.
    Mudhakar, S., Srivatsa, L., Abdelzaher, T.: Mining diverse opinions. In: MILCOM 2012, pp. 1–7 (2012)Google Scholar
  46. 46.
    Palen, L.: Online social media in crisis events. Educause Quarterly (3), 76–78 (2008)Google Scholar
  47. 47.
    Patterson, S., Bamieh, B.: Interaction-driven opinion dynamics in online social networks. In: SOMA 2010, pp. 98–105 (2010)Google Scholar
  48. 48.
    Petrovic, S., Osborne, M., McCreadie, R., Macdonald, C., Ounis, I., Shrimpton, L.: Can Twitter replace Newswire for breaking news? In: ICWSM 2013, pp. 713–716 (2013)Google Scholar
  49. 49.
    Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Transactions on Systems Man and Cybernetics 6, 420–433 (1976)CrossRefMathSciNetMATHGoogle Scholar
  50. 50.
    Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: WWW 2012, pp. 331–340 (2012)Google Scholar
  51. 51.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)Google Scholar
  52. 52.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: GIS 2009: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM, New York (2009)Google Scholar
  53. 53.
    Teitler, B.E., Lieberman, M.D., Panozzo, D., Sankaranarayanan, J., Samet, H., Sperling, J.: Newsstand: a new view on news. In: GIS 2008, pp. 1–10 (2008)Google Scholar
  54. 54.
    Teitler, B.E., Lieberman, M.D., Panozzo, D., Sankaranarayanan, J., Samet, H., Sperling, J.: Newsstand: a new view on news. In: GIS 2008: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10. ACM, New York (2008)Google Scholar
  55. 55.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61, 2544–2558 (2010)CrossRefGoogle Scholar
  56. 56.
  57. 57.
    Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: CIKM 2011, pp. 1031–1040 (2011)Google Scholar
  58. 58.
    Wu, M.: The big data fallacy and why we need to collect even bigger data. TechCrunch (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Divyakant Agrawal
    • 1
  • Ceren Budak
    • 1
  • Amr El Abbadi
    • 1
  • Theodore Georgiou
    • 1
  • Xifeng Yan
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations