Roberto Interdonato
ABSTRACT
Lurkers are silent members of a social network (SN) who gain benefit from others' information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they are social capital holders. Within this view, a major goal is to delurk such users, i.e., to encourage them to more actively be involved in the SN. Despite delurking strategies have been conceptualized in social science and human-computer interaction research, no computational approach has been so far defined to turn lurkers into active participants in the SN. In this work we fill this gap by presenting a delurking-oriented targeted influence maximization problem under the linear threshold (LT) model. We define a novel objective function, in terms of the lurking scores associated with the nodes in the final active set, and we show it is monotone and submodular. We provide an approximate solution by developing a greedy algorithm, named DEvOTION, which computes a k- node set that maximizes the value of the delurking-capital-based objective function, for a given minimum lurking score threshold. Results on SN datasets of different sizes have demonstrated the significance of our delurking approach via LT-based targeted influence maximization.
- J. Bishop, "Increasing participation in online communities: a framework for human-computer interaction," Computers in Human Behavior, 23:1881--1893, 2007. Google Scholar
Digital Library
- F. Celli, F. M. L. D. Lascio, M. Magnani, B. Pacelli, and L. Rossi, "Social Network Data and Practices: The Case of Friendfeed," in Proc. SBP, 2010, pp. 346--353. Google ScholarDigital Library
- N. Edelmann, "Reviewing the definitions of "lurkers" and some implications for online research," Cyberpsychology, Behavior, and Social Networking, 16(9):645--649, 2013.Google ScholarCross Ref
- A. Goyal, W. Lu, and L. V. S. Lakshmanan, "SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model," in Proc. IEEE ICDM, 2011, pp. 211--220. Google ScholarDigital Library
- A. Guille, H. Hacid, C. Favre, and D. A. Zighed, "Information diffusion in online social networks: a survey," SIGMOD Rec., 42(2):17--28, 2013. Google ScholarDigital Library
- B. Guler, B. Varan, K. Tutuncuoglu, M. S. Nafea, A. A. Zewail, A. Yener, and D. Octeau, "Optimal strategies for targeted influence in signed networks," in Proc. ASONAM, 2014, pp. 906--911.Google Scholar
- J. Guo, P. Zhang, C. Zhou, Y. Cao, and L. Guo, "Personalized influence maximization on social networks," in Proc. CIKM, 2013, pp. 199--208. Google ScholarDigital Library
- D. Kempe, J. M. Kleinberg, and E. Tardos, "Maximizing the spread of influence through a social network," in Proc. KDD, 2003, pp. 137--146. Google ScholarDigital Library
- C. Lagnier, L. Denoyer, E. Gaussier, and P. Gallinari, "Predicting information diffusion in social networks using content and user's profiles," in Proc. ECIR, 2013, pp. 74--85. Google ScholarDigital Library
- H. Li, S. S. Bhowmick, A. Sun, and J. Cui, "Conformity-aware influence maximization in online social networks," The VLDB Journal, 24:117--141, 2015. Google ScholarDigital Library
- J. J. McAuley and J. Leskovec, "Learning to Discover Social Circles in Ego Networks," in Proc. NIPS, 2012, pp. 548--556.Google ScholarDigital Library
- N. Sun, P. P.-L. Rau, and L. Ma, "Understanding lurkers in online communities: a literature review," Computers in Human Behavior, 38:110--117, 2014. Google ScholarDigital Library
- A. Tagarelli and R. Interdonato, "Lurking in social networks: topology-based analysis and ranking methods," Social Network Analysis and Mining, 4(230), 2014.Google Scholar
- A. Tagarelli and R. Interdonato, "Understanding lurking behaviors in social networks across time," in Proc. ASONAM, 2014, pp. 51--55.Google Scholar
- A. Tagarelli and R. Interdonato, ""Who's out there?": Identifying and Ranking Lurkers in Social Networks," in Proc. ASONAM, 2013, pp. 215--222. Google ScholarDigital Library
- F. Tang, Q. Liu, H. Zhu, E. Chen, and F. Zhu, "Diversified social influence maximization," in Proc. ASONAM, 2014, pp. 455--459.Google ScholarCross Ref
- D. Yang, H. Hung, W. Lee, and W. Chen, "Maximizing acceptance probability for active friending in online social networks," in Proc. KDD, 2013, pp. 713--721. Google ScholarDigital Library
- J. Zhou, Y. Zhang, and J. Cheng, "Preference-based mining of top-k influential nodes in social networks," Future Generation Computer Systems, 31:40--47, 2014. Google ScholarDigital Library
- "Got to have faith!": The DEvOTION algorithm for delurking in social networks
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