Skip to main content
SpringerLink
Log in

Tools for Analytics and Cognition for Crowd Journalism Application

  • Conference paper
  • First Online:
e-Infrastructure and e-Services for Developing Countries (AFRICOMM 2021)

  • 425 Accesses

Abstract

Businesses and service consumers should take advantage of social media’s ability to adapt their marketing campaigns to achieve a long-term strategic advantage. Setting quantitative and attainable expectations is critical to the progress of every marketing or business endeavour. The development of tools for analytics and cognition (TAC) is essential for customers and providers to increase productivity and inject intelligent insights into operational and mission-critical social media businesses through driven analytics. In this paper, the developed tools provide guided analytics software for intelligent aggregation, cognition and interactive visualization with a monitoring dashboard for concrete crowd journalism use cases. The provider receives an approach to a guided analytic dashboard filled with meaningful business visualization predictions. Among the other things, he can inspect the quantitative metrics for a sharing economy and estimate stakeholders’ channel monetization as a new innovative quantified value by engaging users with trusted content. TAC uses this principle of engagement rate measurements and provides visualization insights for stakeholders to choose the right track for boosting their business.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://articonf.eu/crowd-journalism-use-case/.

References

  1. Prodan, R., et al.: ARTICONF: towards a smart social media ecosystem in a blockchain federated environment. In: Schwardmann, U., et al. (eds.) Euro-Par 2019. LNCS, vol. 11997, pp. 417–428. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48340-1_32

    Chapter  Google Scholar 

  2. Karadimce, A., Paunkoska (Dimoska), N., Capeska Bogatinoska, D., Marina, N., Nandal, A.: Guided analytics software for smart aggregation, cognition, and interactive visualisation. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds.) Innovations in Computational Intelligence and Computer Vision. AISC, vol. 1189, pp. 521–528. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-6067-5_59

  3. BitYoga: Blockchain for social media - Blockchain as a Service (BaaS). ARTICONF Blog, 30 March 2021. https://articonf.eu/blockchain-for-social-media/

  4. Shi, Z., Zhou, H., Hu, Y., Koulouzis, S., Rubia, C., Zhao, Z.: Co-located and orchestrated network fabric (CONF): an automated cloud virtual infrastructure for social network applications. Presented at the Large Scale Distributed Virtual Environments, EuroPar 2019, August 2019. https://doi.org/10.5281/ZENODO.3679385

  5. Saurabh, N., et al.: The ARTICONF approach to decentralized car-sharing. Blockchain: Res. Appl. 1(1–2), 100013 (2021). https://doi.org/10.1016/j.bcra.2021.100013. ISSN 2096-7209

    Article  Google Scholar 

  6. Waters, J., Lester, L.: The Everything Guide to Social Media: All You Need to Know About Participating in Today’s Most Popular Online Communities. Karen Cooper (2010)

    Google Scholar 

  7. Harrin, H.: Social media for project managers. Project Management Institute (2010)

    Google Scholar 

  8. Herbreteau, V., Salem, G., Souris, M., Hugot, J.-P., Gonzalez, J.-P.: Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health Place 13(2), 400–403 (2007). https://doi.org/10.1016/j.healthplace.2006.03.003

    Article  Google Scholar 

  9. Boulil, K., Bimonte, S., Pinet, F.: Conceptual model for spatial data cubes: a UML profile and its automatic implementation. Comput. Stand. Interfaces 38, 113–132 (2015). https://doi.org/10.1016/j.csi.2014.06.004

    Article  Google Scholar 

  10. Sarwat, M.: Interactive and scalable exploration of big spatial data - a data management perspective. In: 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, USA, pp. 263–270 (2015). https://doi.org/10.1109/MDM.2015.67

  11. Brandusescu, A., Sieber, R.E., Jochems, S.: Confronting the hype: the use of crisis mapping for community development. Converg.: Int. J. Res. New Media Technol. 22(6), 616–632 (2016). https://doi.org/10.1177/1354856515584320

    Article  Google Scholar 

  12. Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote. Sens. 65(6), 550–557 (2010). https://doi.org/10.1016/j.isprsjprs.2010.06.005

    Article  Google Scholar 

  13. Ahn, J., Taieb-Maimon, M., Sopan, A., Plaisant, C., Shneiderman, B.: Temporal visualization of social network dynamics: prototypes for nation of neighbors. In: Salerno, J., Yang, S.J., Nau, D., Chai, S.-K. (eds.) SBP 2011. LNCS, vol. 6589, pp. 309–316. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19656-0_43

    Chapter  Google Scholar 

  14. Zaslavsky, A., Jayaraman, P.P., Krishnaswamy, S.: ShareLikesCrowd: mobile analytics for participatory sensing and crowd-sourcing applications. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 128–135 (2013). https://doi.org/10.1109/ICDEW.2013.6547440

  15. Millen, D.R., Patterson, J.F.: Stimulating social engagement in a community network. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, New York, NY, USA, pp. 306–313 (2002). https://doi.org/10.1145/587078.587121

  16. Fedak, V.: Top 4 popular big data visualization tools. Towards Data Science (2018). https://towardsdatascience.com/top-4-popular-big-data-visualization-tools-4ee945fe207d. Accessed 11 Apr 2019

  17. Akshaya, H.L., Nisarga, J.S., Vidya, J., Veena, K.: A basic introduction to DevOps tools. Int. J. Comput. Sci. Inf. Technol. 6(3), 2349–2353 (2015)

    Google Scholar 

  18. Iyer, R., Agrawal, P.: Social return on investment - measuring the impact. Int. Res. J. Bus. Manage. VIII(10) (2015)

    Google Scholar 

  19. Salverda, M.: Social Return on Investment (2016). https://www.betterevaluation.org/approach/SROI

  20. Nicholls, J., Lawlor, E., Neitzert, E., Goodspeed, T: A guide to social return on investment (revised). The SROI Network (UK) (2012). https://www.socialvalueuk.org/resources/sroi-guide/

  21. Teixeira, L., Amorim, I., Ulisses Silva, A., Correia Lopes, J., Filipe, V.: A new approach to crowd journalism using a blockchain-based infrastructure. In: Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia (MoMM 2020), pp. 170–178. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3428690.3429159

Download references

Acknowledgement

This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825134, the ARTICONF Project (https://articonf.eu/).

Author information

Authors and Affiliations

  1. University of Information Science and Technology, 6000, Ohrid, Macedonia

    Natasa Paunkoska, Atanas Hristov, Aleksandar Karadimce, Ninoslav Marina & Mirsat Sefidanoski

Authors
  1. Natasa Paunkoska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atanas Hristov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aleksandar Karadimce
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ninoslav Marina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mirsat Sefidanoski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natasa Paunkoska .

Editor information

Editors and Affiliations

  1. State University of Zanzibar, Zanzibar, Tanzania

    Yahya H. Sheikh

  2. State University of Zanzibar, Zanzibar, Tanzania

    Idris A. Rai

  3. State University of Zanzibar, Zanzibar, Tanzania

    Abubakar D. Bakar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paunkoska, N., Hristov, A., Karadimce, A., Marina, N., Sefidanoski, M. (2022). Tools for Analytics and Cognition for Crowd Journalism Application. In: Sheikh, Y.H., Rai, I.A., Bakar, A.D. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-031-06374-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06374-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06373-2

  • Online ISBN: 978-3-031-06374-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation