ABSTRACT
The profile of online social relationship is fundamental in online collaboration studies. A comprehensive profile should describe the nature of a relationship on two levels: properties and connotation. Current studies mainly characterize the connotation of a social relationship with positive/negative signs or fixed categories which are not sufficient to reveal the specific connotation of a certain relationship. Interactive language is believed to be closely related to the nature of social relationships according to sociolinguistics. In this work, we propose to semantically model the connotation of social relationships with interactive language between individuals. We connect the features and topics of the interactive language with the connotation of the social relationship. The experimental results on English emails and Chinese microblogs reveal that the new method can profile the social relationships with more meaningful details.
- Aggarwal, C.C.: Social Network Data Analytics. Springer Publishing Company (2011)Google ScholarDigital Library
- Zhang, T.G.: The Theory and Practice of the Research Methods of Social Linguistics. (in Chinese) Peking University, Beijing (2008)Google Scholar
- Bernstein, B.B.: Theoretical Studies towards a Sociology of Language. Springer Publishing Company. Routledge (2003)Google Scholar
- Li, X., Fang, H., Zhang, J.: Rethinking the link prediction problem in signed social networks. In Proceedings of the Thirty-First AAAI Conference on Articial Intelligence, 49554956. (2017)Google ScholarDigital Library
- B., Wang, Y. S., Yu, P., Zhang.: Investigation of the Subjective Asymmetry of Social Interrelationship with Interactive Language. In Proceedings of the 25th international conference on World Wide Web. (2016)Google Scholar
- Zhang, J., Wang, C., Yu, P.S., Wang, J.: Learning latent friendship propagation networks with interest awareness for link prediction. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp.63–72. ACM (2013)Google Scholar
- Zhang, J., Wang, C., Wang, J.: Who proposed the relationship?: recovering the hidden directions of undirected social networks. In: Proceedings of the 23rd international conference on World wide web, pp.807–818. ACM (2014)Google Scholar
- Chiang, K.Y., Natarajan, N., Tewari, A., Dhillon, I.S.: Exploiting longer cycles for link prediction in signed networks. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp.1157–1162. ACM (2011)Google Scholar
- Leskovec, J.: How users evaluate things and each other in social media. J. (2012)Google Scholar
- Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: International Conference on World Wide Web, WWW 2010, pp.981–990. Raleigh, North Carolina (2009)Google ScholarDigital Library
- 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
- 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
- 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
- Tang, J., Chang, Y., Aggarwal, C., Liu, H.: A survey of signed network mining in social media. ACM Computing Surveys (CSUR) 49(3):42. (2016)Google Scholar
- Agrawal, P., Garg, V.K., Narayanam, R.: Link Label Prediction in Signed Social Networks. In: International Joint Conference on Artificial Intelligence (2013)Google Scholar
- Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp.1361–1370. ACM (2013)Google Scholar
- Kunegis, J., Schmidt, S., Lommatzsch, A., Lerner, J., De Luca, E.W., Albayrak, S.: Spectral analysis of signed graphs for clustering, prediction and visualization. In: Proceedings of Siam International Conference on Data Mining, pp.559–559. (2009)Google Scholar
- Ye, J., Cheng, H., Zhu, Z., Chen, M.: Predicting positive and negative links in signed social networks by transfer learning. In: Proceedings of the 22nd internationalconference on World Wide Web, pp.1477–1488. ACM (2013)Google Scholar
- Tang, J., Chang, S., Aggarwal, C.: Negative link prediction in social media. Computer Science. J. Com. Sci. 87–96. (2014)Google Scholar
- Yang, S.H., Smola, A.J., Long, B., Zha, H., Chang, Y.: Friend or frenemy?: predicting signed ties in social networks. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp.555– 564. ACM (2013)Google Scholar
- 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
- X. Li, H. Fang, Q. Yang, and J. Zhang, “Who is your best friend?: Ranking social network friends according to trust relationship,” In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP, July 08-11, 2018, pp. 301–309.Google ScholarDigital Library
- X. Li, H. Fang, and J. Zhang, “File: A novel framework for predicting social status in signed networks,” In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.Google ScholarCross Ref
- P. Xu, W. Hu, J. Wu, and B. Du, “Link prediction with signed latent factors in signed social networks,” In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD, 2019, pp. 1046–1054.Google ScholarDigital Library
- Z. Wang, T. Chen, J. S. J. Ren, W. Yu, H. Cheng, and L. Lin, “Deep reasoning with knowledge graph for social relationship understanding,” In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, pp. 1021–1028.Google ScholarCross Ref
- T. Chen, W. Yu, R. Chen, and L. Lin, “Knowledge-embedded routing network for scene graph generation,” In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2019, pp. 6163–6171.Google ScholarCross Ref
- Holmes, J., Meyerhoff, M., Meyerhoff, M., Mullany, L., Stockwell, P., Llamas, C., : An Introduction to Sociolinguistics, 4th Edition. Routledge, London and New York (2013)Google ScholarCross Ref
- Peng M., Huang J.J., Zhu J.H.: Mass of Short Texts Clustering and Topic Extraction based on Frequent Item sets. (in Chinese) J. Com. Res. Dev. 52, 1941–1953 (2015)Google Scholar
- Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. J. Acm. Sig. Rec. 29, 1–12 (2000)Google ScholarDigital Library
- 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, 2012, pp.161–165.Google Scholar
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