Study of Social Trends Prediction using AI

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 02 | Page : –
    By

    Smaran Das,

  • Anamika Gupta,

  1. Assistant Professor, University of Dublin, Trinity College, Dublin University of Delhi, Shaheed Sukhdev College of Business Studies, Delhi, India
  2. Professor, University of Delhi, Shaheed Sukhdev College of Business Studies, Delhi, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_197193’);});Edit Abstract & Keyword

AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlates in a variety of available datasets from social media to economic indicators to public records, and fundamentally changes decision-making based on data for business, politics, and public health.  As data-driven decision-making becomes informed by the outcomes of AI forecasting with the potential for major influences on public health and institutions, rapid adoption creates ethical challenges including algorithmic bias, privacy issues, transparency, and misuse of AI-generated predictions of public opinion. This chapter considers foundational AI methods for predicting social trends such as sentiment analysis, topic modeling, ARIMA (Autoregressive integrated moving average), LSTMs (Long Short term Memory Networks), and agent-based models and considers their use for practical applications such as consumer behavior analysis, election outcome forecasting, tracking pandemic events, and crime prevention. The chapter considers emerging developments such as Generative AI, multimodal analytics, and explainable AI (XAI), which will further the use of AI to improve accuracy of forecasting while addressing challenges of interpretability. After which, the ethical and regulatory implications of AI are addressed, and considerations for fairness-aware algorithms, techniques for privacy, and the importance of human oversight to foster responsible use of AI are presented.

Keywords: Artificial Intelligence(AI), Social Trend Prediction, Ethical AI, NLP, Time-Series Forecasting, Bias, Explainability

How to cite this article:
Smaran Das, Anamika Gupta. Study of Social Trends Prediction using AI. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Smaran Das, Anamika Gupta. Study of Social Trends Prediction using AI. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_197193’);});Edit

References

  1. Succi S, Coveney PV. Big data: the end of the scientific method? Philos Trans A Math Phys Eng Sci. 2019;377(2142):20180145.
  2. Gerlach M, Peixoto TP, Altmann EG. A network approach to topic models. Sci Adv. 2018;4(7):eaaq1360.
  3. Okeleke PA, Ajiga D, Folorunsho SO, Ezeigweneme C. Predictive analytics for market trends using AI: a study in consumer behavior. Int J Eng Res Updates. 2024;7(1):36–49.
  4. Salganik MJ. Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press; 2019.
  5. Lazer DM, Baum MA, Benkler Y, et al. The science of fake news. Science. 2018;359(6380):1094–1096
  6. Ziems C, Held W, Shaikh O, et al. Can large language models transform computational social science? Computational Linguistics. 2024;50(1):237–291
  7. Mallipeddi RR, Kumar S, Sriskandarajah C, Zhu Y. A framework for analyzing influencer marketing in social networks: selection and scheduling of influencers. Management Science. 2022;68(1):75–104.
  8. Varol O, Ferrara E, Davis CA, et al. Online human-bot interactions: Detection, estimation, and characterization. Proceedings of the International AAAI Conference on Web and Social Media. 2017;11(1):280–289.
  9. Wang Z, Hale S, Adelani DI, et al. Demographic inference and representative population estimates from multilingual social media data. In: Proceedings of the WWW Conference; 2019. p. 2056–67.
  10. Mekimah S, Zighed R, Mili K, Bengana I. Business intelligence in organizational decision-making: a bibliometric analysis of research trends and gaps (2014–2024). Discov Sustain. 2024;5(1):1–18.
  11. Zareie A, Sakellariou R. Influence maximization in social networks: a survey of behaviour-aware methods. Soc Netw Anal Min. 2023;13(1):78.
  12. Cambria E, Xing F, Thelwall M, Welsch R. Sentiment analysis as a multidisciplinary research area. IEEE Trans Artif Intell. 2022;3(2):1–4.
  13. Jelodar H, Wang Y, Yuan C, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78:15169–211.
  14. Wang D, Li T, Deng P, et al. A generalized deep learning clustering algorithm based on non-negative matrix factorization. ACM Trans Knowl Discov Data. 2023;17(7):1–20.
  15. Li J, Sun A, Han J, Li C. A survey on deep learning for named entity recognition. IEEE Trans Knowl Data Eng. 2020;34(1):50–70.
  16. Brownlee J. Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery; 2018 .
  17. Greff K, Srivastava RK, Koutník J, et al. LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems. 2017;28(10):2222–2232.
  18. Souravlas S, Sifaleras A, Tsintogianni M, Katsavounis S. A classification of community detection methods in social networks: a survey. Int J Gen Syst. 2021;50(1):63–91.
  19. Alali Y, Harrou F, Sun Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci Rep. 2022;12(1):2467.
  20. Antelmi A, Cordasco G, D’Ambrosio G, et al. Experimenting with agent-based model simulation tools. Appl Sci. 2022;13(1):13.
  21. Gkikas DC, Theodoridis PK. AI in consumer behavior. In: Bourbakis NG, editor. Advances in Artificial Intelligence-based Technologies. Vol. 1. Cham: Springer; 2021. p. 147–76.
  22. Lazer DM, Baum MA, Benkler Y, et al. The science of fake news. Science. 2018;359(6380):1094–6.
  23. Benkler Y, Faris R, Roberts H. Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics. Oxford University Press; 2018.
  24. Chen S, Yu J, Chamouni S, et al. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions. BMC Med. 2024;22(1):354.
  25. Cortes ALL, Silva CF. Artificial intelligence models for crime prediction in urban spaces. Mach Learn Appl Int J. 2021;8.
  26. Pearl J. The limitations of opaque learning machines. In: Brockman J, editor. Possible Minds. New York: Penguin Press; 2019. p. 13–19.
  27. González-Bailón S. Social science in the era of big data. Annual Review of Sociology. 2017;43:19–39.
  28. Gilpin LH, Bau D, Yuan BZ, et al. Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE; 2018. p. 80–9.
  29. Mehrabi N, Morstatter F, Saxena N, et al. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54(6):1–35.
  30. Hollingshead W, Quan-Haase A, Blank G. Representativeness and bias in social media research: quantitative and qualitative approaches to sampling. In: Sloan L, Quan-Haase A, editors. The Sage Handbook of Social Media Research Methods. 2nd ed. London: Sage; 2022. p. 79–90.
  31. Tulli S, Aha DW, editors. Explainable Agency in Artificial Intelligence: Research and Practice. Boca Raton, FL: CRC Press; 2024.
  32. Hongladarom S. Review of: Zuboff S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: Public Affairs; 2019. Inf Commun Soc. 2023.
Ahead of Print Subscription Review Article
Volume 12
02
Received 22/04/2025
Accepted 25/06/2025
Published 19/07/2025
Publication Time 88 Days
128

[first_name] [last_name]

My IP

PlumX Metrics