IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0168125.html
   My bibliography  Save this article

Learning from Bees: An Approach for Influence Maximization on Viral Campaigns

Author

Listed:
  • C Prem Sankar
  • Asharaf S.
  • K Satheesh Kumar

Abstract

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.

Suggested Citation

  • C Prem Sankar & Asharaf S. & K Satheesh Kumar, 2016. "Learning from Bees: An Approach for Influence Maximization on Viral Campaigns," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-15, December.
    • Handle: RePEc:plo:pone00:0168125
    DOI: 10.1371/journal.pone.0168125
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168125
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0168125&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0168125?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Wu, Fang & Huberman, Bernardo A. & Adamic, Lada A. & Tyler, Joshua R., 2004. "Information flow in social groups," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 327-335.
    3. Yuqing Zhu & Weili Wu & Yuanjun Bi & Lidong Wu & Yiwei Jiang & Wen Xu, 2015. "Better approximation algorithms for influence maximization in online social networks," Journal of Combinatorial Optimization, Springer, vol. 30(1), pages 97-108, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuan Yang & Xiao Li & Daning Hu & Harry Jiannan Wang, 2021. "Differential impacts of social influence on initial and sustained participation in open source software projects," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1133-1147, September.
    2. Langley, David J. & Hoeve, Maarten C. & Ortt, J. Roland & Pals, Nico & van der Vecht, Bob, 2014. "Patterns of Herding and their Occurrence in an Online Setting," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 16-25.
    3. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    4. Massimiliano Zanin & David Papo & Miguel Romance & Regino Criado & Santiago Moral, 2016. "The topology of card transaction money flows," Papers 1605.04938, arXiv.org.
    5. Shu-Hsun Ho & Yu-Ling Lin & Robert Carlson Patrick, 2015. "Participant Motivations In A Social Media Community Page," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 9(4), pages 67-75.
    6. Pablo Gomez‐Carrasco & Giovanna Michelon, 2017. "The Power of Stakeholders' Voice: The Effects of Social Media Activism on Stock Markets," Business Strategy and the Environment, Wiley Blackwell, vol. 26(6), pages 855-872, September.
    7. Ko, J. & Kwon, H.W. & Kim, H.S. & Lee, K. & Choi, M.Y., 2014. "Model for Twitter dynamics: Public attention and time series of tweeting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 142-149.
    8. Alberto Lopez & Eva Guerra & Beatriz Gonzalez & Sergio Madero, 2020. "Consumer sentiments toward brands: the interaction effect between brand personality and sentiments on electronic word of mouth," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 203-223, December.
    9. Rajković, Borislav & Đurić, Ivan & Zarić, Vlade & Glauben, Thomas, 2021. "Gaining trust in the digital age: The potential of social media for increasing the competitiveness of small and medium enterprises," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 13(4).
    10. Krishen, Anjala S. & Dwivedi, Yogesh K. & Bindu, N. & Kumar, K. Satheesh, 2021. "A broad overview of interactive digital marketing: A bibliometric network analysis," Journal of Business Research, Elsevier, vol. 131(C), pages 183-195.
    11. Tatiana David-Negre & Arminda Almedida-Santana & Juan M. Hernández & Sergio Moreno-Gil, 2018. "Understanding European tourists’ use of e-tourism platforms. Analysis of networks," Information Technology & Tourism, Springer, vol. 20(1), pages 131-152, December.
    12. Choi-Meng Leong & Alexa Min-Wei Loi & Steve Woon, 2022. "The influence of social media eWOM information on purchase intention," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(2), pages 145-157, June.
    13. Budzinski, Oliver & Gaenssle, Sophia, 2018. "The economics of social media stars: An empirical investigation of stardom, popularity, and success on YouTube," Ilmenau Economics Discussion Papers 112, Ilmenau University of Technology, Institute of Economics.
    14. Nisar, Tahir M. & Prabhakar, Guru & Strakova, Lubica, 2019. "Social media information benefits, knowledge management and smart organizations," Journal of Business Research, Elsevier, vol. 94(C), pages 264-272.
    15. Claussen, Jörg & Engelstätter, Benjamin & Ward, Michael R., 2014. "Susceptibility and influence in social media word-of-mouth," ZEW Discussion Papers 14-129, ZEW - Leibniz Centre for European Economic Research.
    16. Syed Sardar Muhammad & Bidit Lal Dey & Vishanth Weerakkody, 2018. "Analysis of Factors that Influence Customers’ Willingness to Leave Big Data Digital Footprints on Social Media: A Systematic Review of Literature," Information Systems Frontiers, Springer, vol. 20(3), pages 559-576, June.
    17. Martin Quinn & Theodore Lynn & Stephen Jollands & Binesh Nair, 2016. "Domestic Water Charges in Ireland - Issues and Challenges Conveyed through Social Media," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3577-3591, August.
    18. Duan, Huijue Kelly & Vasarhelyi, Miklos A. & Codesso, Mauricio & Alzamil, Zamil, 2023. "Enhancing the government accounting information systems using social media information: An application of text mining and machine learning," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    19. Aladwani, Adel M., 2015. "Facilitators, characteristics, and impacts of Twitter use: Theoretical analysis and empirical illustration," International Journal of Information Management, Elsevier, vol. 35(1), pages 15-25.
    20. Santiago, A. & Benito, R.M., 2008. "Connectivity degrees in the threshold preferential attachment model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(10), pages 2365-2376.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0168125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.