Advertisement

Measuring Bidirectional Subjective Strength of Online Social Relationship by Synthetizing the Interactive Language Features and Social Balance (Short Paper)

  • Baixiang Xue
  • Bo WangEmail author
  • Yanshu Yu
  • Ruifang He
  • Yuexian Hou
  • Dawei Song
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

In online collaboration, instead of the objective strength of social relationship, recent study reveals that the two participants can have different subjective opinions on the relationship between them, and the opinion can be investigated with their interactive language on this relationship. However, two participants’ bidirectional opinions in collaboration is not only determined by their interaction on this relationship, but also influenced by the adjacent third-party partners. In this work, we define the two participants’ opinions as the subjective strength of their relationship. To measure the bidirectional subjective strength of a social relationship, we propose a computational model synthetizing the features from participants’ interactive language and the adjacent balance in social network. Experimental results on real collaboration in Enron email dataset verify the effectiveness of the proposed model.

Keywords

Social relationship Subjective strength Interactive language Balance theory 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (U1736103), National Natural Science Foundation of China (Key Program, U1636203), the state key development program of China (2017YFE0111900) and National Natural Science Foundation of China (61472277).

References

  1. 1.
    Aggarwal, C.C.: Social Network Data Analytics. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-1-4419-8462-3CrossRefzbMATHGoogle Scholar
  2. 2.
    Wang, B., Yu, Y.S., Zhang, P.: Investigation of the subjective asymmetry of social interrelationship with interactive language. In: International Conference on World Wide Web (2016)Google Scholar
  3. 3.
    Sundararajan, A.: The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism. The MIT Press, Cambridge (2016)Google Scholar
  4. 4.
    Granovetter, M.S.: The strength of weak ties. J. Soc. 78, 1360–1380 (1973)Google Scholar
  5. 5.
    Sintos, S., Tsaparas, P.: Using strong triadic closure to characterize ties in social networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1466–1475. ACM (2014)Google Scholar
  6. 6.
    Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: International Conference on World Wide Web, pp. 981–990. WWW 2010, Raleigh, North Carolina, USA (2010)Google Scholar
  7. 7.
    Zhuang, J., Mei, T., Hoi, S.C., Hua, X.S., Li, S.: Modeling social strength in social media community via kernel-based learning. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 113–122. ACM (2009)Google Scholar
  8. 8.
    Kahanda, I., Neville, J.: Using Transactional Information to Predict Link Strength in Online Social Networks. In: International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA (2009)Google Scholar
  9. 9.
    Adali, S., Sisenda, F., Magdon-Ismail, M.: Actions speak as loud as words: Predicting relationships from social behavior data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 689–698. ACM (2012)Google Scholar
  10. 10.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World wide web, pp. 641–650. ACM (2010)Google Scholar
  11. 11.
    West, R., Paskov, H. S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. J. Epr. Arx. (2014)Google Scholar
  12. 12.
    Bramsen, P., Escobar-Molano, M., Patel, A., Alonso, R.: Extracting social power relationships from natural language. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics Human Language Technologies, vol. 1, pp. 773–782 (2011)Google Scholar
  13. 13.
    Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335 (2006)Google Scholar
  14. 14.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271 (2004)Google Scholar
  15. 15.
    Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405 (2011)Google Scholar
  16. 16.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296 (2011)Google Scholar
  17. 17.
    Huang, B., Kimmig, A., Getoor, L., Golbeck, J.: A flexible framework for probabilistic models of social trust. In: Greenberg, Ariel M., Kennedy, William G., Bos, Nathan D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 265–273. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37210-0_29CrossRefGoogle Scholar
  18. 18.
    Habermas, J., McCarthy, T.: The Theory of Communicative Action. Beacon Press, Boston (1985)Google Scholar
  19. 19.
    Sapir, E.: The status of linguistics as a science. J. Lan. 195–197 (1929)Google Scholar
  20. 20.
    Holmes, J., Meyerhoff, M., Meyerhoff, M., Mullany, L., Stockwell, P., Llamas, C., et al.: An Introduction to Sociolinguistics, 4th edn. Routledge, London, New York (2013)Google Scholar
  21. 21.
    Heider, F.: The Psychology of Interpersonal Relations. Psychology Press, London (2013)CrossRefGoogle Scholar
  22. 22.
    Wang, B., Sun, Y.J., Han, B., Hou, Y.X., Song, D.W.: Extending the balance theory by measuring bidirectional opinions with interactive language. In: International Conference on World Wide Web (2017)Google Scholar
  23. 23.
    Agarwal, A., Omuya, A., Harnly, A., Rambow, O.: A comprehensive gold standard for the Enron organizational hierarchy. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 161–165. Association for Computational Linguistics (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Baixiang Xue
    • 1
  • Bo Wang
    • 1
    Email author
  • Yanshu Yu
    • 1
  • Ruifang He
    • 1
  • Yuexian Hou
    • 1
  • Dawei Song
    • 1
  1. 1.College of Intelligence and ComputingTianjin UniversityTianjinChina

Personalised recommendations