ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Visual analysis of semantic measuring of online social relationships

  • 1.

    School of Computer Science and Technology, Tianjin University, Tianjin 300000, China

  • 2.

    Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300000, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.04.007
  • Received Date: 31 May 2017
  • Rev Recd Date: 25 June 2017
  • Publish Date: 30 April 2018
    Abstract

  • Abstract

    In the traditional social relationships analysis, the attribute of social relation is regarded as objective and independent of the subjective cognition of the participant. However, in the social computing studies related to subjective behaviors, subjective features are often more important than objective features. The semantics of social relationship with the interactive language between individuals is visualized. Based on The key features of the interactive language in the theory of social linguistics, four language features to describe the semantics of social relationships are calculated, including frequency, length, fluency and sentiment polarity. By measuring and distinguishing personal language habits, the semantic measuring of social relationship more appropriate. To make semantic measuring more understand, a visual analysis system is implemented for online social relationships using Email data as a case, and the factors related to the semantic features of online social relationships are priminarily analyzed.

    Abstract

    In the traditional social relationships analysis, the attribute of social relation is regarded as objective and independent of the subjective cognition of the participant. However, in the social computing studies related to subjective behaviors, subjective features are often more important than objective features. The semantics of social relationship with the interactive language between individuals is visualized. Based on The key features of the interactive language in the theory of social linguistics, four language features to describe the semantics of social relationships are calculated, including frequency, length, fluency and sentiment polarity. By measuring and distinguishing personal language habits, the semantic measuring of social relationship more appropriate. To make semantic measuring more understand, a visual analysis system is implemented for online social relationships using Email data as a case, and the factors related to the semantic features of online social relationships are priminarily analyzed.
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    • References(19)
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    SAPIR E.The status of linguistics as a science[J]. Linguistic Society of America,1929, 5(4): 207-214.
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    HOLMES J. An Introduction to Sociolinguistics[M]. 4ed, Rutledge, 2013.
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    WANG B, YU Y S, ZHANG P. Investigation of the subjective asymmetry of social interrelationship with interactive language[C]// Proceedings of the 25rd International Conference on World Wide Web. Geneva, Switzerland: ACM Press, 2016: 121-122.
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    [1]
    王飞跃，李晓晨，毛吉文,等. 社会计算的基本方法与应用[M]. 2版，杭州: 浙江大学出版社, 2013.
    [2]
    ZHANG J, WANG C K, WANG J M. Who proposed the relationship? Recovering the hidden directions of undirected social networks[C]// Proceedings of the 23rd International Conference on World Wide Web. Seoul: ACM Press, 2014: 807-817.
    [3]
    CHIANG K Y, NATARAJAN N, TEWARI A. Exploiting longer cycles for link prediction in signed networks[C]// Proceedings of the 20th ACM International Conference on Information and knowledge management. Glasgow, UK: ACM Press, 2011: 1157-1162.
    [4]
    LESKOVEC J. How users evaluate each other in social media[C]// Proceedings of the Web Search Data Mining. Seattle: ACM Press, 2012.
    [5]
    YUAN N J, ZHONG Y, ZHANG F Z, et al. Who will reply to/retweet this tweet? The dynamics of friendships and online social interactions[C]// Proceedings of the Web Search Data Mining. San Francisco: ACM Press, 2016: 3-12.
    [6]
    GRANOVETTER M S. The strength of weak ties[J]. The American Journal of Sociology, 1973, 78(6): 1360-1380.
    [7]
    JIONGQIAN LIANG, DEEPAK AJWANI, PATRICK NICHOLSON, et al. What links Alice and Bob? Matching and ranking semantic patterns in heterogeneous networks[C]// Proceedings of the 25th International Conference on World Wide Web. Québec, Canada: IEEE Press, 2016: 879-889.
    [8]
    ZHUANG J F, MEI T, HOI S C H, et al. Modeling social strength in social media community via kernel-based learning[C]// Proceedings of the 19th ACM international conference on Multimedia. Scottsdale: ACM Press, 2011: 113-122.
    [9]
    SINTOS S, TSAPARAS P. Using strong triadic closure to characterize ties in social networks[C]// Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM Press, 2014: 1466-1475.
    [10]
    ADALI S, SISENDA F, MAGDON-ISMAIL M. Actions speak as loud as words: Predicting relationships from social behavior data[C]// Proceedings of the 21rd International Conference on World Wide Web. Lyon, France: ACM Press, 2012: 689-698.
    [11]
    KUNEGIS J, PREUSSE J, SCHWAGEREIT F. What is the added value of negative links in online social networks?[C]// Proceedings of the 22rd International Conference on World Wide Web. Rio de Janeiro, Brazil: ACM Press, 2013: 727-736.
    [12]
    YE J H, CHENG H, ZHU Z, et al. Predicting positive and negative links in signed social networks by transfer learning[C]// Proceedings of the 22rd International Conference on World Wide Web. Rio de Janeiro, Brazil: ACM Press, 2013: 1477-1488.
    [13]
    KUNEGIS J, SCHMIDT S, LOMMATZSCH A. Spectral analysis of signed graphs for clustering, prediction and visualization[C]// Proceedings of the SIAM International Conference on Data Mining. Columbus, USA: ACM Press, 2010: 559- 559.
    [14]
    TANG J L, CHANG S Y, AGGARWAL C, et al. Negative link prediction in social media[C]// Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Shanghai, China: ACM Press, 2015: 87-96.
    [15]
    WESTR, PASKOV H S, LESKOVEC J, et al. Exploiting social network structure for person-to-person sentiment analysis[J]. IEEE Transactions of the Association for Computational Linguistics, 2014: arXiv:1409.2450.
    [16]
    SAPIR E.The status of linguistics as a science[J]. Linguistic Society of America,1929, 5(4): 207-214.
    [17]
    HOLMES J. An Introduction to Sociolinguistics[M]. 4ed, Rutledge, 2013.
    [18]
    WANG B, YU Y S, ZHANG P. Investigation of the subjective asymmetry of social interrelationship with interactive language[C]// Proceedings of the 25rd International Conference on World Wide Web. Geneva, Switzerland: ACM Press, 2016: 121-122.
    [19]
    WANG B, SUN Y J, HAN B, et al. Extending the balance theory by measuring bidirectional opinions with interactive language[C]// Proceedings of the 26rd International Conference on World Wide Web. Perth, Australia: ACM Press, 2017: 849-850.
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