Study of Social Trends Prediction Using AI

Year : 2025 | Volume : 12 | Issue : 03 | Page : 19 29
    By

    Smaran Das,

  • Anamika Gupta,

  1. Student, Department of Computer Science, University of Dublin, Trinity College, Dublin, University of Delhi, Shaheed Sukhdev College of Business Studies, Rohini, Sector 16, Delhi, Delhi, India
  2. Professor, Department of Computer Science, University of Delhi, Shaheed Sukhdev College of Business Studies, Rohini, Sector 16, Delhi, Delhi, India

Abstract

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 correlations 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 study 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 study 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 this, 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

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Smaran Das, Anamika Gupta. Study of Social Trends Prediction Using AI. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):19-29.
How to cite this URL:
Smaran Das, Anamika Gupta. Study of Social Trends Prediction Using AI. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):19-29. Available from: https://journals.stmjournals.com/joaira/article=2025/view=217219


References

  1. Succi S, Coveney PV. Big data: the end of the scientific method? Philos Trans R Soc A. 2019 Apr 8; 377(2142): 20180145.
  2. Gerlach M, Peixoto TP, Altmann EG. A network approach to topic models. Sci Adv. 2018 Jul 18; 4(7): eaaq1360.
  3. Okeleke PA, Ajiga D, Folorunsho SO, Ezeigweneme C. Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates (IJERU). 2024 Aug; 7(1): 36–49.
  4. Salganik MJ. Bit by bit: Social research in the digital age. Princeton University Press; Princeton, New Jersey, United States. 2019 Aug 6.
  5. Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, Schudson M. The science of fake news. Science. 2018 Mar 9; 359(6380): 1094–6.
  6. Ziems C, Held W, Shaikh O, Chen J, Zhang Z, Yang D. Can large language models transform computational social science? Comput Linguist. 2024 Mar 1; 50(1): 237–91.
  7. Mallipeddi RR, Kumar S, Sriskandarajah C, Zhu Y. A framework for analyzing influencer marketing in social networks: selection and scheduling of influencers. Manag Sci. 2022 Jan; 68(1): 75–104.
  8. Varol O, Ferrara E, Davis C, Menczer F, Flammini A. Online human-bot interactions: Detection, estimation, and characterization. In Proceedings of the international AAAI conference on web and social media. 2017 May 3; 11(1): 280–289.
  9. Wang Z, Hale S, Adelani DI, Grabowicz P, Hartman T, Flöck F, Jurgens D. Demographic inference and representative population estimates from multilingual social media data. In The world wide web conference. 2019 May 13; 2056–2067.
  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 Dec 31; 5(1): 532.
  11. Zareie A, Sakellariou R. Influence maximization in social networks: a survey of behaviour-aware methods. Soc Netw Anal Min. 2023 Apr 25; 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, Feng X, Jiang X, Li Y, Zhao L. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019 Jun 15; 78(11): 15169–211.
  14. Wang D, Li T, Deng P, Zhang F, Huang W, Zhang P, Liu J. A generalized deep learning clustering algorithm based on non-negative matrix factorization. ACM Trans Knowl Discov Data. 2023 May 4; 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 Mar 17; 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; Victoria, Australia. 2018 Aug 30.
  17. Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J. LSTM: A search space odyssey. IEEE Trans Neural Netw Learn Syst. 2016 Jul 8; 28(10): 2222–32.
  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 Jan 2; 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 Feb 14; 12(1): 2467.
  20. Antelmi A, Cordasco G, D’Ambrosio G, De Vinco D, Spagnuolo C. Experimenting with agent-based model simulation tools. Appl Sci. 2022 Dec 20; 13(1): 13.
  21. Gkikas DC, Theodoridis PK. AI in consumer behavior. In: Advances in Artificial Intelligence-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis. Vol. 1. Cham: Springer International Publishing; 2021 Oct 3; 147–176.
  22. Benkler Y, Faris R, Roberts H. Network propaganda: Manipulation, disinformation, and radicalization in American politics. Oxford University Press; Oxford, England. 2018 Sep 17.
  23. Chen S, Yu J, Chamouni S, Wang Y, Li Y. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions. BMC Med. 2024 Sep 2; 22(1): 354.
  24. Cortes AL, Silva CF. Artificial intelligence models for crime prediction in urban spaces. Machine Learning and Applications: An International Journal (MLAIJ). 2021 Mar; 8(1): 1–13.
  25. Pearl J. The limitations of opaque learning machines. In: Brockman J, editor. Possible Minds. New York: Penguin Press; 2019 May: 13–19.
  26. González‐Bailón S. Social science in the era of big data. Policy Internet. 2013 Jun; 5(2): 147–60.
  27. Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). 2018 Oct 1; 80–89.
  28. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021 Jul 13; 54(6): 1–35.
  29. Hollingshead W, Quan-Haase A, Blank G. Representativeness and bias in social media research: Quantitative and qualitative approaches to sampling. The Sage handbook of social media research methods. SAGE Publications Ltd.; 2022; London. 79–90.
  30. Tulli S, Aha DW, editors. Explainable Agency in Artificial Intelligence: Research and Practice. CRC Press; Boca Raton, Florida. 2024 Jan 22.
Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 22/04/2025
Accepted 25/06/2025
Published 19/07/2025
Publication Time 88 Days
386

Login


My IP

PlumX Metrics