De-lurking in Online Communities Using Repost Behavior Prediction Method
الموضوعات :
1 - Urmia University of Technology
الکلمات المفتاحية: De-Lurking , Post Similarity , Lurker , Online Community,
ملخص المقالة :
Nowadays, with the advent of social networks, a big change has occurred in the structure of web-based services. Online community (OC) enable their users to access different type of Information, through the internet based structure anywhere any time. OC services are among the strategies used for production and repost of information by users interested in a specific area. In this respect, users become members in a particular domain at will and begin posting. Considering the networking structure, one of the major challenges these groups face is the lack of reposting behavior. Most users of these systems take up a lurking position toward the posts in the forum. De-lurking is a type of social media behavior where a user breaks an "online silence" or habit of passive thread viewing to engage in a virtual conversation. One of the proposed ways to improve De-Lurking is the selection and display of influential posts for each individual. Influential posts are so selected as to be more likely reposted by users based on each user's interests, knowledge and characteristics. The present article intends to introduce a new method for selecting k influential posts to ensure increased repost of information. In terms of participation in OCs, users are divided into two groups of posters and lurkers. Some solutions are proposed to encourage lurking users to participate in reposting the contents. Based on actual data from Twitter and actual blogs with respect to reposts, the assessments indicate the effectiveness of the proposed method.
[1] E. Wenger, “Communities of Practice and Social Learning Systems,” Organization, vol. 7. pp. 225–246, 2000.#
[2] E. Wenger, community of practice: a brief introduction, vol. 15. 1998.#
[3] M. O. Weil, “Community building: building community practice,” Soc. Work, vol. 41, p. 481, 1996.#
[4] L. Clarke, “The POD model: Using communities of practice theory to conceptualise student teachers’ professional learning online,” Comput. Educ., vol. 52, no. 3, pp. 521–529, 2009.#
[5] C. K. Chang, G. D. Chen, and L. Y. Li, “Constructing a community of practice to improve coursework activity,” Comput. Educ., vol. 50, pp. 235–247, 2008.#
[6] T. Hoang and E. Lim, “Virality and Susceptibility in Information Diffusions,” Artif. Intell., no. 2010, pp. 146–153, 2012.#
[7] I. Uysal and W. B. Croft, “User oriented tweet ranking: A filtering approach to microblogs,” in International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 2261–2264.#
[8] D. Ienco, F. Bonchi, and C. Castillo, “The meme ranking problem: Maximizing microblogging virality,” in Proceedings - IEEE International Conference on Data Mining, ICDM, 2010, pp. 328–335.#
[9] L. Gou, X. (Luke) Zhang, H.-H. Chen, J.-H. Kim, and C. L. Giles, “Social network document ranking,” Proc. 10th Annu. Jt. Conf. Digit. Libr. - JCDL ’10, pp. 313–322, 2010.#
[10] M. Cha, H. Haddai, F. Benevenuto, and K. P. Gummadi, “Measuring User Influence in Twitter : The Million Follower Fallacy,” Int. AAAI Conf. Weblogs Soc. Media, pp. 10–17, 2010.#
[11] D. M. Romero, W. Galuba, S. Asur, and B. a. Huberman, “Influence and passivity in social media,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6913 LNAI, no. PART 3, pp. 18–33, 2011.#
[12] D. Gruhl, D. Liben-Nowell, R. Guha, and A. Tomkins, “Information diffusion through blogspace,” ACM SIGKDD Explorations Newsletter, vol. 6. pp. 43–52, 2004.#
[13] E. Adar and L. a. Adamic, “Tracking information epidemics in blogspace,” Proc. - 2005 IEEE/WIC/ACM Int. Web Intell. WI 2005, vol. 2005, pp. 207–214, 2005.#
[14] J. Leskovec, L. Adamic, and B. Huberman, “The Dynamics of Viral Marketing,” ACM Trans. Web, vol. 1, no. 1, pp. 1–39, 2007.#
[15] E. Sun, I. Rosenn, C. a Marlow, and T. M. Lento, “Gesundheit ! Modeling Contagion through Facebook News Feed Mechanics of Facebook Page Diffusion,” Proc. Third Int. ICWSM Conf., no. 2000, pp. 146–153, 2009.#
[16] E. Bakshy, B. Karrer, and L. A. Adamic, “Social Influence and the Diffusion of User Created Content,” in Electronic Commerce, 2009, pp. 325–334.#
[17] P. Domingos and M. Richardson, “Mining the Network Value of Customers,” in Proceedings of the Seventh {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, 2001, pp. 57–66.#
[18] M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” Proc. eighth ACM SIGKDD Int. Conf. Knowl. Discov. data Min. KDD 02, vol. 02, no. 3, p. 61, 2002.#
[19] D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’03, 2003, p. 137.#
[20] A. Goyal, F. Bonchi, and L. V. S. Lakshmanan, “A data-based approach to social influence maximization,” Proc. VLDB Endow., vol. 5, pp. 73–84, 2011.#
[21] U. Feige, V. S. Mirrokni, and J. Vondrák, “Maximizing non-monotone submodular functions,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, 2007, pp. 461–471.#
[22] S. Stieglitz and L. Dang-Xuan, “Emotions and Information Diffusion in Social Media - Sentiment of Microblogs and Sharing Behavior,” J. Manag. Inf. Syst., vol. 29, no. 4, p. 217, 2013.#
[23] S. Ye and S. F. Wu, “Measuring message propagation and social influence on Twitter.com,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6430 LNCS, pp. 216–231.#
[24] D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman, “Influence and passivity in social media,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6913 LNAI, pp. 18–33.#
[25] M. Magnani, D. Montesi, and L. Rossi, “Information Propagation Analysis in a Social Network Site,” 2010 Int. Conf. Adv. Soc. Networks Anal. Min., pp. 296–300, 2010.#
[26] A. Anderson, D. Huttenlocher, J. Kleinberg, and J. Leskovec, “Effects of user similarity in social media,” in Proceedings of the fifth ACM international conference on Web search and data mining WSDM 12, 2012, p. 703.#
[27] A. Goyal, F. Bonchi, and L. V. S. Lakshmanan, “Learning influence probabilities in social networks,” Proc. third ACM Int. Conf. Web search data Min. - WSDM ’10, p. 241, 2010.#
[28] N. E. Friedkin and E. C. Johnsen, “Social influence and opinions,” The Journal of Mathematical Sociology, vol. 15. pp. 193–206, 1990.#
[29] J. Tang, J. Sun, C. Wang, and Z. Yang, “Social Influence Analysis in Large-scale Networks,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp. 807–816.#
[30] M. Nagarajan, H. Purohit, and A. Sheth, “A Qualitative Examination of Topical Tweet and Retweet Practices,” Artif. Intell., pp. 295–298, 2010.#
[31] C. G. Knight and L. K. Kaye, “‘To tweet or not to tweet?’ A comparison of academics’ and students’ usage of Twitter in academic contexts,” Innov. Educ. Teach. Int., no. April 2015, pp. 1–11, 2014.#
[32] C. Haeussler, “Information-sharing in academia and the industry: A comparative study,” Res. Policy, vol. 40, no. 1, pp. 105–122, 2011.#
[33] W. S. Hwang, S. W. Kim, D. H. Bae, and Y. J. Do, “Post ranking algorithms in blog environment,” in Proceedings of the 2008 2nd International Conference on Future Generation Communication and Networking, FGCN 2008, 2008, vol. 2, pp. 64–67.#
