Big Data Analysis to Inferring Nationality Using X Social Network without GPS

Tareq Abed  Mohammed

Authors

Tareq Abed Mohammed University of Kirkuk, College of Veterinary Medicine, Kirkuk, Iraq

Keywords:

Big Data, Database, Data Analysis, Machine Learning, KNIME, Social Media Analyzing

Abstract

Inferring user’s nationality from social media destinations gets to be a hot inquire about topic. In this paper we propose a modern and basic data analysis and algorithm to predict the nationality of X social network client without utilizing any GPS data like past proposed algorithms. The proposed algorithm employs the X social network user friends location data as it were. In spite of the fact that as it were around 30% of the X clients compose their location data in important form, we demonstrate that this percent is sufficient to de-cide the root nation or the nationality of any X client. Our proposed algorithm classifies more than 90% of the X client in our collecting dataset. We utilize within the proposed algorithm six nations but this work can effectively be generalized to incorporate all the world nations.

References

M. Aria, C. Cuccurullo, L. D’Aniello, M. Misuraca, and M. Spano, “Thematic analysis as a new culturomic tool: The social media coverage on COVID-19 pandemic in Italy,” Sustainability, vol. 14, no. 6, art. no. 3643, 2022.

C. Qian, N. Mathur, N. H. Zakaria, R. Arora, V. Gupta, and M. Ali, “Understanding public opinions on social media for financial sentiment analysis using AI-based techniques,” Information Processing & Management, vol. 59, no. 6, art. no. 103098, 2022.

M. T. Refsnes, Whitman on TweetDeck: Community and Self Through Walt Whitman’s Leaves of Grass and Blogging, M.S. thesis, Univ. of Bergen, Bergen, Norway, 2022.

V. Mathur, C. Lustig, and E. Kaziunas, “Disordering datasets: Sociotechnical misalignments in AI-mediated behavioral health,” Proc. ACM Hum.-Comput. Interact., vol. 6, no. CSCW2, pp. 1–33, 2022.

C. Cross and M. Lee, “Exploring fear of crime for those targeted by romance fraud,” Victims & Offenders, vol. 17, no. 5, pp. 735–755, 2022.

X. Zhao et al., “Estimating wildfire evacuation decision and departure timing using large-scale GPS data,” Transportation Research Part D: Transport and Environment, vol. 107, art. no. 103277, 2022, doi: 10.1016/j.trd.2022.103277.

T. A. Mohammed, S. Alhayli, S. Albawi, and A. D. Duru, “Intelligent database interface techniques using semantic coordination,” in Proc. 1st Int. Scientific Conf. Engineering Sciences–3rd Scientific Conf. Engineering Science (ISCES), Jan. 2018, pp. 13–17.

X. Huang, L. Xing, F. Dernoncourt, and M. J. Paul, “Multilingual Twitter corpus and baselines for evaluating demographic bias in hate speech recognition,” arXiv preprint, arXiv:2002.10361, 2020.

J. Cornelisse, “Inferring neuroticism of Twitter users by utilizing their following interests,” in Proc. 3rd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, Dec. 2020, pp. 1–10.

J. Kim, A. Sîrbu, G. Rossetti, and F. Giannotti, “Characterising different communities of Twitter users: Migrants and natives,” in Complex Networks & Their Applications X, Cham, Switzerland: Springer, 2022, pp. 130–141.

H. Mubarak, S. A. Chowdhury, and F. Alam, “Arabgend: Gender analysis and inference on Arabic Twitter,” arXiv preprint, arXiv:2203.00271, 2022.

I. Jun et al., “Evaluating the perceptions of pesticide use, safety, and regulation and identifying common pesticide-related topics on Twitter,” Integrated Environmental Assessment and Management, vol. 19, no. 6, pp. 1581–1599, 2023.

H. Al-Bayaty, T. Mohammed, A. Ghareeb, and W. Wang, “City-scale energy demand forecasting using machine learning-based models: A comparative study,” in Proc. 2nd Int. Conf. Data Science, E-Learning and Information Systems, Dec. 2019, pp. 1–9.

R. Chaturvedi and S. Chaturvedi, “It’s all in the name: A character-based approach to infer religion,” Political Analysis, vol. 32, no. 1, pp. 34–49, 2024.

P. Vyas, G. Vyas, and G. Dhiman, “Ruemo—The classification framework for Russia–Ukraine war-related societal emotions on Twitter through machine learning,” Algorithms, vol. 16, no. 2, art. no. 69, 2023.

“Early prediction of stroke risk using machine learning approaches and imbalanced data,” NTU-JET, vol. 4, no. 1, Mar. 2025, doi: 10.56286/1vf19469.

A. K. Abbas, T. A. Mohammed, O. Bayat, and O. N. Ucan, “The prediction of fusion degree of international groups from their Twitter accounts,” in Proc. Int. Conf. Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1–8, doi: 10.1109/ICEngTechnol.2017.8308185.

KNIME Analytics Platform, “KNIME,” [Online]. Available: https://www.knime.com. Accessed: Feb. 20, 2024.

B. Maraza-Quispe et al., “A predictive model implemented in KNIME based on learning analytics for timely decision making in virtual learning environments,” Int. J. Information and Education Technology, vol. 12, no. 2, pp. 91–99, 2022.

K. K. Abbo and H. H. Mohamed, “New scaled conjugate gradient algorithm for training artificial neural networks based on pure conjugacy condition,” Kirkuk Journal of Science, vol. 10, no. 3, pp. 230–241, 2015.

E. Z. Mohammed, “Proposed classification system by using artificial neural network,” Kirkuk Journal of Science, vol. 10, no. 3, pp. 59–78, 2015.