Advertisement

A Novel Approach for Predicting the Outcome of Request in RAOP Dataset

  • Amreen AhmadEmail author
  • Tanvir Ahmad
  • Abhishek Bhatt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 847)

Abstract

In today’s era, online social communities such as Q&A sites are widely used for asking favors, so it would be beneficial to formulate a technique that would help in predicting the success of the response. The objective of the paper is to enhance the accuracy of prediction of the success of altruistic request that follows the same approach as used by ADJ (Proceedings of AAAI International Conference on Web and Social Media, ICWSM, 2014 [1]. Three more features are proposed, i.e., topic, role, and centrality in addition to the features proposed by ADJ [1]) to capture user’s interaction in the past and topic effect on the prediction of response. We also propose a graph-based success prediction (GSP) model that uses feature weights and uses the underlying graph structure for the propagation to predict the outcome of a request. Experiments were conducted on the RAOP dataset which belongs to sub-community of Reddit.com using GSP, and it outperformed ADJ and other baseline methods using limited training data.

Keywords

Success prediction Altruistic request Social interactions 

References

  1. 1.
    Althoff, T., Danescu-Niculescu-Mizil, C., Jurafsky, D. (ADJ): How to ask for a favor: a case study on the success of altruistic request. In: Proceedings of AAAI International Conference on Web and Social Media, ICWSM (2014)Google Scholar
  2. 2.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pp. 44–54 (2006)Google Scholar
  3. 3.
    Freedman, D.A.: Statistical Models: Theory and Practice, pp. 128 (Cambridge University Press, 2009)Google Scholar
  4. 4.
    Scripps, J., Tan, P.N., Esfahanian, A.H.: Exploration of link structure and community-based node roles in network analysis. In: Proceedings of the IEEE International Conference on Data Mining, ICDM, pp. 649–654 (2007)Google Scholar
  5. 5.
    Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. Proc. Nat. Acad. Sci. U.S.A. (PNAS) 109(16), 5962–5966 (2012)CrossRefGoogle Scholar
  6. 6.
    Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273297 (1995)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia

Personalised recommendations