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Recruiting the K-most influential prospective workers for crowdsourcing platforms

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Abstract

Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K-most influential crowd workers. In this paper, IVMF is exemplified in the domain of personal care industry to show its importance through a real-life case.

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Notes

  1. In clustering techniques, each cluster has a cluster center. Each object of the dataset is assigned to the nearest cluster center.

  2. Each content has a context vector too which shows what contexts does it contain. For an example, a news is spreading in the social network. It has both the social context and political context together.

  3. The algorithm computes the propagation (or the number of nodes which are finally activated) of each node. So it has a “for” loop and we calculate the number of iterations that are repeating in this loop.

References

  1. Hays S, Page SJ, Buhalis D (2013) Social media as a destination marketing tool: its use by national tourism organisations. Curr Issues Tour 16:211–239

    Article  Google Scholar 

  2. Saravanakumar M, Sugantha Lakshmi T (2012) Social media marketing. Life Sci J 9:4444–4451

    Google Scholar 

  3. Jin L, Chen Y, Wang T, Hui P, Vasilakos AV (2013) Understanding user behavior in online social networks: a survey. IEEE Commun Mag 51:144–150

    Google Scholar 

  4. Ferguson R (2008) Word of mouth and viral marketing: taking the temperature of the hottest trends in marketing. J Consum Mark 25:179–182

    Article  Google Scholar 

  5. Hui A, Wong PW-C, Fu K-W (2015) Evaluation of an online campaign for promoting help-seeking attitudes for depression using a Facebook advertisement: an online randomized controlled experiment. JMIR Ment Health 2:897

    Article  Google Scholar 

  6. Wilson C, Boe B, Sala A, Puttaswamy KP, Zhao BY (2009) User interactions in social networks and their implications. In: Proceedings of the 4th ACM European conference on computer systems, pp 205–218

  7. Doan A, Ramakrishnan R, Halevy AY (2011) Crowdsourcing systems on the world-wide web. Commun ACM 54:86–96

    Article  Google Scholar 

  8. Ross J, Irani L, Silberman M, Zaldivar A, Tomlinson B (2010) Who are the crowdworkers? shifting demographics in mechanical turk. In: CHI’10 extended abstracts on human factors in computing systems, pp 2863–2872

  9. Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, pp 57–66

  10. Narayanam R, Narahari Y (2011) A shapley value-based approach to discover influential nodes in social networks. IEEE Trans Autom Sci Eng 8:130–147

    Article  Google Scholar 

  11. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 137–146

  12. Saito K, Kimura M, Ohara K, Motoda H (2010) Behavioral analyses of information diffusion models by observed data of social network. In: International conference on social computing, behavioral modeling, and prediction, pp 149–158

  13. Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 199–208

  14. Estevez PA, Vera P, Saito K (2007) Selecting the most influential nodes in social networks. In: International joint conference on neural networks, 2007. IJCNN 2007, pp 2397–2402

  15. Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management, pp 233–242

  16. Chen N (2009) On the approximability of influence in social networks. SIAM J Discrete Math 23:1400–1415

    Article  MathSciNet  Google Scholar 

  17. Wang Y, Cong G, Song G, Xie K (2010) Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1039–1048

  18. Zhang Y, Wang Z, Xia C (2010) Identifying key users for targeted marketing by mining online social network. In: 2010 IEEE 24th international conference on advanced information networking and applications workshops (WAINA), pp 644–649

  19. Aslay C, Barbieri N, Bonchi F, Baeza-Yates RA (2014) Online topic-aware influence maximization queries. In: EDBT, pp 295–306

  20. He X, Song G, Chen W, Jiang Q (2012) Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the 2012 SIAM international conference on data mining, pp 463–474

  21. Zhou J, Zhang Y, Cheng J (2014) Preference-based mining of top-K influential nodes in social networks. Future Gener Comput Syst 31:40–47

    Article  Google Scholar 

  22. Wang Y, Feng X (2009) A potential-based node selection strategy for influence maximization in a social network. In: International conference on advanced data mining and applications, pp 350–361

  23. Leskovec J, Krause A, Guestrin C, Faloutsos C, Van Briesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 420–429

  24. Chen Y-C, Peng W-C, Lee S-Y (2012) Efficient algorithms for influence maximization in social networks. Knowl Inf Syst 33:577–601

    Article  Google Scholar 

  25. Li Y-M, Lai C-Y (2013) A diffusing path planning mechanism for marketing information propagation over social media. In: 2013 46th Hawaii international conference on system sciences (HICSS), pp 2863–2871

  26. Ordanini A, Miceli L, Pizzetti M, Parasuraman A (2011) Crowd-funding: transforming customers into investors through innovative service platforms. J Serv Manag 22:443–470

    Article  Google Scholar 

  27. Lin H, Davis J, Zhou Y (2010) Ontological services using crowdsourcing. In: 21st Australasian conference on information systems

  28. Verma R, Ruj S (2014) Security services using crowdsourcing. Proc Comput Sci 32:505–512

    Article  Google Scholar 

  29. Alonso O, Rose DE, Stewart B (2008) Crowdsourcing for relevance evaluation. In: ACM SigIR forum, pp 9–15

  30. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17:107–145

    Article  Google Scholar 

  31. Esmaeili L, Nasiri M, Minaei-Bidgoli B (2013) Analyzing persian social networks: an empirical study. In: IGI global, pp 167–172

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Maryam Shahsavari & Alireza Hashemi Golpayegani

  2. School of Business, Australian Defence Forces Academy, University of New South Wales Canberra, Canberra, ACT, Australia

    Morteza Saberi

  3. Faculty of Engineering and Information Technology, School of Software and Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Farookh Khadeer Hussain

Authors
  1. Maryam Shahsavari
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  2. Alireza Hashemi Golpayegani
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  3. Morteza Saberi
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  4. Farookh Khadeer Hussain
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Correspondence to Maryam Shahsavari.

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Shahsavari, M., Golpayegani, A.H., Saberi, M. et al. Recruiting the K-most influential prospective workers for crowdsourcing platforms. SOCA 12, 247–257 (2018). https://doi.org/10.1007/s11761-018-0247-z

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  • DOI: https://doi.org/10.1007/s11761-018-0247-z

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