Published on: April 2026
BHABANIPUR ASSEMBLY ELECTION DIGITAL TWIN: A GEO-SPATIAL MULTIMODAL ARTIFICIAL INTELLIGENCE STRUCTURE
Poushali Das Anindita Naha Raima Bhattacharyya Trishna Chakraborty Arpita Chakraborty Chandra Karmakar Siddhartha Chatterjee Subhajit Ojha Abhinaba Bhattacharyya Ritwika Ghosh
Article Status
Available Documents
Abstract
Constituency-level election prediction is difficult given the dynamic, non-linear and complex nature of voting behavior that is shaped by behavioral profiles, spatial variance, emotional narratives and temporal variations. Traditional approaches, such as opinion polls and static statistical analyses, often fail to capture these changes in real time. This study suggests the Bhabanipur Assembly Election Digital Twin as a nascent multimodal geo-spatial digital phenotyping approach that will create a digital twin of the constituency by dynamically building a virtual model of the constituency from publicly available digital data sources.
How to Cite this Paper
Das, P., Naha, A., Bhattacharyya, R., Chakraborty, T., Chakraborty, A., Karmakar, C., Chatterjee, S., Ojha, S., Bhattacharyya, A. & Ghosh, R. (2026). Bhabanipur Assembly Election Digital Twin: A GEO-Spatial Multimodal Artificial Intelligence Structure. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.004
Das, Poushali, et al.. "Bhabanipur Assembly Election Digital Twin: A GEO-Spatial Multimodal Artificial Intelligence Structure." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.004.
Das, Poushali,Anindita Naha,Raima Bhattacharyya,Trishna Chakraborty,Arpita Chakraborty,Chandra Karmakar,Siddhartha Chatterjee,Subhajit Ojha,Abhinaba Bhattacharyya, and Ritwika Ghosh. "Bhabanipur Assembly Election Digital Twin: A GEO-Spatial Multimodal Artificial Intelligence Structure." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.004.
References
[1] A. Graefe, J. S. Armstrong, R. J. Jones, and A. G. Cuzán,“Combining forecasts: An application to elections,” International Journal of Forecasting, vol. 30, no. 1, pp. 43–54, 2014.
[2] N. Silver,
The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t. New York, NY, USA: Penguin, 2012.
[3] A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe,
“Predicting elections with Twitter,” ICWSM, pp. 178–185, 2010.
[4] B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith,
“From tweets to polls,” ICWSM, pp. 122–129, 2010.
[5] A. Ceron, L. Curini, and S. M. Iacus,
“Every tweet counts?,” New Media & Society, vol. 16, no. 2, pp. 340–358, 2014.
[6] D. Gayo-Avello,
“A balanced survey on election prediction using Twitter data,” arXiv, 2012.
[7] B. Liu,
Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012.
[8] E. Cambria, B. Schuller, Y. Xia, and C. Havasi,
“Opinion mining and sentiment analysis,” IEEE Intelligent Systems, vol. 28, no. 2, pp. 15–21, 2013.
[9] L. Young and S. Soroka,
“Affective news,” Political Communication, vol. 29, no. 2, pp. 205–231, 2012.
[10] S. M. Mohammad and P. D. Turney,
“Crowdsourcing a word–emotion lexicon,” Computational Intelligence, vol. 29, no. 3, pp. 436–465, 2013.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 03 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

