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Journal of Computational Social Dynamics

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Published

2022-10-05

Issue

Vol. 7 No. 4 (2022): Journal of Computational Social Dynamics-4th-Q

Section

Articles

How to Cite

Rahman , A., & Anas, R. M. H. (2022). Big Data for Social Good: A Review of Applications in Poverty, Health, and Environmental Sustainability. Journal of Computational Social Dynamics, 7(4), 11–21. Retrieved from https://vectoral.org/index.php/JCSD/article/view/43

Big Data for Social Good: A Review of Applications in Poverty, Health, and Environmental Sustainability

Aisha Rahman 

Department of Social Sciences, Lahore University of Management Sciences (LUMS), Pakistan 

Rana Muhammad Huzaifa Anas

University of the Punjab

Keywords: Big data analytics, Social good, Poverty, Healthcare, Environmental sustainability, Ethics, Participatory approaches

Abstract

Big data analytics refers to advanced techniques for deriving insights from large, diverse, rapidly generated datasets. It combines predictive modelling, data mining, machine learning, statistics, and database methods to uncover patterns and generate intelligent findings. While initially focused on commercial applications, big data analytics holds tremendous promise for driving social change across domains like healthcare, education, environmental sustainability, and international development. This paper reviews applications of big data analytics across three areas critical for social good: poverty, health, and the environment. For each domain, we provide background context, highlight innovative examples of big data driving impact, summarise key opportunities and challenges, and outline future directions. Major opportunities of big data analytics are real-time monitoring, evidence-based policymaking, early warning systems, prevention versus reaction, optimised resource allocation, personalised services, and improved coordination. However, there are ethical concerns around privacy, security, transparency, and perpetuating biases. Priorities for future research include balancing computational insights with social science knowledge, participatory approaches, capacity building, transparency in data and modeling, and legal-regulatory frameworks for ethical usage of data. While big data analytics enables data-driven humanitarian action, nuanced frameworks are imperative to ensure analytical insights are converted into equitable and just social impact. This review synthesises applications, key opportunities and challenges, and priorities for future research to guide big data analytics across the interdisciplinary intersections of technology, social sciences, policy, ethics, and international development.

Author Biography

Rana Muhammad Huzaifa Anas, University of the Punjab

 

 

 

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Submission Guidelines for the Journal of Computational Social Dynamics General Guidelines:

  1. Article Types: We accept a variety of article types including Original Research Articles, Review Papers, Short Reports, and Opinion Pieces.

  2. Language: Manuscripts must be written in English, and should be clear, concise, and free of jargon.

  3. Length: There is no strict word limit, but Original Research Articles should generally be between 4,000 and 10,000 words, including references.

  4. Formatting: Manuscripts should be submitted as a single Word or LaTeX document, formatted in double-spaced 12-point font.

  5. Abstract: An abstract of 250-300 words is required for all submissions, summarizing the key objectives, methods, findings, and implications of the research.

Manuscript Structure:

  • Title Page: Must include the title, author names, affiliations, and contact information for the corresponding author.

  • Introduction: Provide background, state the problem, and outline objectives.

  • Methods: Detail the methods and tools used in your research.

  • Results: Present the findings of your study.

  • Discussion: Interpret results, discuss their impact, and suggest future research directions.

  • Conclusion: Summarize the main points of the paper.

  • References: Citations should be formatted according to the APA/IEEE styles.

Ethical Guidelines:

Authors must ensure that their research adheres to ethical standards and that informed consent has been obtained for any human or animal subjects involved.

Plagiarism:

The Journal of Computational Social Dynamics uses plagiarism detection software. Any manuscript found to include plagiarized content will be rejected.

Peer Review Process:

All submissions undergo a double-blind peer review process. Authors can expect initial feedback within six to eight weeks.

Submission Process:
  1. Prepare your manuscript according to the guidelines.
  2. Submit your manuscript through our online portal at.
  3. After submission, you will receive a confirmation email with a manuscript ID for tracking purposes.
Revisions:

If revisions are requested, authors must resubmit the revised manuscript within the specified timeframe.

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The journal has a nominal publication fee, waived for authors who lack funding. Details are available on our website.

For any questions or concerns, please contact the editorial office at editorial@vectoral.org. Thank you for considering the Journal of Computational Social Dynamics as a venue for your research.

Published by Vectoral Publishing, the Journal of Computational Social Dynamics maintains high standards in peer-review and scientific inquiry. Authors who wish to submit manuscripts should adhere to the guidelines detailed on our website, www.vectoral.org/jcsd. Vectoral Publishing does not bear any responsibility for the accuracy of information or for any outcomes related to the use of the research published. For editorial correspondence and submissions, please reach out to editorial@vectoral.org.