KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Constructing Negative Links from Multi-facet of Social Media

  • Li, Lin (School of Computer Science, Zhengzhou University of Aeronautics) ;
  • Yan, YunYi (School of Aerospace Science and Technology, Xidian University) ;
  • Jia, LiBin (School of Computer Science, Zhengzhou University of Aeronautics) ;
  • Ma, Jun (School of Information and Electronics, Beijing Institute of Technology)
  • Received : 2016.08.20
  • Accepted : 2017.01.22
  • Published : 2017.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

Keywords

References

  1. Andreas M Kaplan and Michael Haenlein, "Users of the world, unite! the challenges and opportunities of social media," Business horizons, 53(1):59-68, 2010. https://doi.org/10.1016/j.bushor.2009.09.003
  2. Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, "Social Media Mining: An Introduction," Cambridge University Press, 2014.
  3. David Liben-Nowell and Jon Kleinberg, "The link prediction problem for social networks," in Proc. of 12th International Conference on Information and Knowledge Management.
  4. Aditya Krishna Menon and Charles Elkan, "Link prediction via matrix factorization, Machine Learning and Knowledge Discovery in Databases, pp. 437-452, Springer, 2011.
  5. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, The pagerank citation ranking: Bringing order to the web, 1999.
  6. Xiaoli Li and Bing Liu, "Learning to classify texts using positive and unlabeled data," IJCAI, volume 3, pp. 587-592, 2003.
  7. Charles Elkan and Keith Noto, "Learning classi_ers from only positive and unlabeled data," in Proc. of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 213-220. ACM, 2008.
  8. Ryan N Lichtenwalter, Jake T Lussier, and Nitesh V Chawla, "New perspectives and methods in link prediction," in Proc. of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 243-252. ACM, 2010.
  9. Ramanthan Guha, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins, "Propagation of trust and distrust," in Proc. of the 13th international conference on World Wide Web, pp. 403-412. ACM, 2004.
  10. Jiliang Tang, Xia Hu, and Huan Liu, "Is distrust the negation of trust? the value of distrust in social media," in Proc. of ACM Hypertext conference, 2014.
  11. Yanhua Li,Wei Chen, YajunWang, and Zhi-Li Zhang, "Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships," in Proc. of the sixth ACM international conference on Web search and data mining, pp. 657-666. ACM, 2013.
  12. Vincent Traag, Yurii Nesterov, and Paul Van Dooren, "Exponential ranking: Taking into account negative links," Social Informatics, pp. 192-202, 2010.
  13. Hao Ma, Haixuan Yang, Michael Lyu, and Irwin King, "Sorec: social recommendation using probabilistic matrix factorization," in Proc. of the 17th ACM conference on Information and knowledge management, pp. 931-940. ACM, 2008.
  14. Yehuda Koren, Robert Bell, and Chris Volinsky, "Matrix factorization techniques for recommender systems," Computer, 42(8):30-37, 2009. https://doi.org/10.1109/MC.2009.263
  15. Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King, "Recommender systems with social regularization," in Proc. of the fourth ACM international conference on Web search and data mining, pp. 287-296. ACM, 2011.
  16. Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg, "Predicting positive and negative links in online social networks," in Proc. of the 19th international conference on World wide web, pp. 641-650. ACM, 2010.
  17. Deng Cai, Chiyuan Zhang, and Xiaofei He, "Unsupervised feature selection for multi-cluster data," in Proc. of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 333-342. ACM, 2010.
  18. Jiliang Tang and Huan Liu, "Unsupervised feature selection for linked social media data," in Proc. of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 904-912. ACM, 2012.
  19. Jiliang Tang, Shiyu Chang, Charu Aggarwaly and Huan Liu, "Negative Link Prediction in Social Media," WSDM'15, February 2-6, Shanghai, China, 2015.
  20. J. Leskovec, D. Huttenlocher and J. Kleiberg, "Predicting Positive and Negative Links in Online Social Networks," in Proc. of WWW 2010, April 26-30, Raleigh, North Carolina, USA, 2010.
  21. M. Hu and B. Liu, "Mining and Summarizing Customer Reviews," in Proc. of KDD'04, August 22-25, Seattle, Washington, USA, 2004.
  22. Thorsten Joachims, Nello Cristianini, and John Shawe-Taylor, "Composite kernels for hypertext categorisation," in Proc. of the 18th International Conference on Machine Learning, 2001.
  23. Mehmet Gonen, Ethem Alpaydin, "Localized Multiple Kernel Learning," in Proc. of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008.
  24. http://www.epinions.com/
  25. http://slashdot.org/
  26. LA Adamic, BA Huberman, AL Barabasi, R Albert, H Heong and G Bianconi, "Power-Law Distribution of the World Wide Web," Science, 287(5461):2115, 2000. https://doi.org/10.1126/science.287.5461.2115a
  27. Jiliang Tang, Xia Hu, and Huan Liu, "2014a. Is Distrust the Negation of Trust? The Value of Distrust in Social Media," in Proc. of ACM Hypertext conference.
  28. James S Coleman, "Social capital in the creation of human capital," American journal of sociology, S95-S120, 1988.
  29. Michael Szell, Renaud Lambiotte, and Stefan Thurner, "Multirelational organization of large-scale social networks in an online world," in Proc. of the National Academy of Sciences 107, 31, 13636-13641, 2010. https://doi.org/10.1073/pnas.1004008107
  30. Dorwin Cartwright and Frank Harary, "Structural balance: a generalization of Heider's theory," Psychological review 63, 5 277, 1956. https://doi.org/10.1037/h0046049
  31. Symeon Papadopoulos, Yiannis Kompatsiaris, Athena Vakali, and Ploutarchos Spyridonos, "Community detection in social media," Data Mining and Knowledge Discovery 24, 515-554, 2012. https://doi.org/10.1007/s10618-011-0224-z
  32. David Kempe, Jon Kleinberg, and Eva Tardos, "Maximizing the spread of influence through a social network," in Proc. of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 137-146, 2003.
  33. Xiaoguang Qi and Brian D Davison, "Web page classification: Features and algorithms," ACM Computing Surveys (CSUR) 41, 2, 12, 2009.
  34. Mahdokht Masaeli, Jennifer G Dy, and Glenn M Fung, "From transformation-based dimensionality reduction to feature selection," in Proc. of the 27th International Conference on Machine Learning (ICML-10), 751-758, 2010.
  35. Rana Forsati, Mehrdad Mahdavi,Mehrnoush Shamsfard, and Mohamed Sarwat, "Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation," ACM Transactions on Information Systems (TOIS) 32, 4, 17, 2014.
  36. Wei Chen, Yajun Wang, and Siyu Yang, "Efficient influence maximization in social networks," in Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 199-208, 2009.
  37. Vikas Sindhwani, Partha Niyogi, and Mikhail Belkin, "Beyond the point cloud: from transductive to semi-supervised learning," in Proc. of the 22nd international conference on Machine learning. ACM, 824-831, 2005.
  38. KiriWagstaff, Claire Cardie, Seth Rogers, Stefan Schrodl, and others, "Constrained k-means clustering with background knowledge," ICML, Vol. 1. 577-584, 2001.
  39. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification 2nd Edition. 2004. John Wiley.
  40. Mohsen Jamali, and Martin Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proc. of the fourth ACM conference on Recommender systems, ACM, 135-142, 2010.
  41. Lise Getoor, and Christopher P Diehl, "Link mining: a survey," ACM SIGKDD Explorations Newsletter, 7(2):3-12, 2005. https://doi.org/10.1145/1117454.1117456