Utilizing Social Media Analytics to Detect Trends in Saudi Arabia’s Evolving Market

Kanwal Aalijah

doi:10.18535/ijsrm/v13i02.ec01

Abstract

Saudi Arabia' faced a swift economic growth and societal transformation under Vision 2030. This offers a unique opportunity to track emerging trends in the region, which will ultimately pave the way for new business and investment possibilities. This paper explores how AI and social media analytics can identify and track trends across sectors such as construction, food and beverage, tourism, technology, and entertainment thereby helping the businesses make informed decisions. By leveraging a tailored AI-driven methodology, we analyzed millions of social media posts each month, classifying discussions and calculating scores to track the trends. The approach not only uncovered the emerging trends but also shows diminishing trends. Our methodology is able to predict the emergence and growth of trends by utilizing social media data. This approach has potential for adaptation in other regions. Ultimately, our findings highlight how ongoing, AI-powered trend analysis can enable more effective, data-informed business and development strategies in an increasingly dynamic environment.

Keywords

Social Media AnalyticsAI-Driven Trend AnalysisSaudi Arabia Market TrendsMachine

References

Ministry of Economy and Planning, "Vision 2030 and Economic Development in Saudi Arabia," Saudi Government Publications, 2020.Google Scholar ↗
J. Chae, J. Chen, T. Zhang, et al., "Social Media Analytics for Regional Trend Analysis," Journal of Business Research, vol. 112, pp. 125-137, 2020.Google Scholar ↗
Alshamsi, A. Qasmi, and A. Mohtaseb, "Consumer Sentiment Analysis for Saudi Arabia Using Social Media Data," Saudi Economic Review, vol. 11, no. 4, pp. 37-54, 2022.Google Scholar ↗
K. Aalijah and R. Irfan, "Scalable Taxonomy Generation and Evolution on Apache Spark," 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Calgary, AB, Canada, 2020, pp. 634-639, doi: 10.1109/DASC-PICom-CBDCom-DOI ↗Google Scholar ↗
CyberSciTech49142.2020.00110.Google Scholar ↗
M. Hussain, M. Alharthi, and M. Hawsawi, "AI and Social Media Trend Analysis in the Middle East," Journal of Middle Eastern Studies, vol. 24, no. 2, pp. 145-162, 2023.xGoogle Scholar ↗
Aalijah, K., Irfan, R., Umar, U., & Nayab, S. (2022). Automatic taxonomy generation and incremental evolution on Apache Spark Parallelization Framework. KIET Journal of Computing and Information Sciences, 5(1), 15–36.Google Scholar ↗
H. Chen, R. Chiang, and V. C. Storey, "Business Intelligence and Analytics: From Big Data to Big Impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, Dec. 2012.Google Scholar ↗
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, 2018.Google Scholar ↗
W. Antoun, R. Baly, and H. Hajj, "AraBERT: Transformer-based Model for Arabic Language Understanding," Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, pp. 9-15, 2020.Google Scholar ↗
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, 2018Google Scholar ↗
Y. Zhang, S. Wu, and K. Liang, "AI-Driven Consumer Insights for Emerging Markets," Technology and Society, vol. 37, pp. 293-306, 2020.Google Scholar ↗
Alshamsi, A. Qasmi, and A. Mohtaseb, "Consumer Sentiment Analysis for Saudi Arabia Using Social Media Data," Saudi Economic Review, vol. 11, no. 4, pp. 37-54, 2022.Google Scholar ↗
D. Jiang, C. Lu, and Y. Sun, "Multimodal Sentiment Analysis on Social Media Data," IEEE Transactions on Multimedia, vol. 23, no. 1, pp. 280-289, 2021.Google Scholar ↗
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016, pp. 164-166.Google Scholar ↗
I. Loshchilov and F. Hutter, "Decoupled Weight Decay Regularization," in Proc. Int. Conf. Learning Representations (ICLR), Vancouver, Canada, Apr. 2019.Google Scholar ↗
M. Grootendorst, "BERTopic: Leveraging BERT embeddings for topic modelling," arXiv preprint, 2020.Google Scholar ↗
M. Grootendorst, "BERTopic: Neural topic modelling with a class- based TF-IDF procedure," arXiv preprint arXiv:2203.05794, 2022Google Scholar ↗
R. J. G. B. Campello, D. Moulavi, and J. Sander, "Density-Based Clustering Based on Hierarchical Density Estimates," in Proc. of the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining,Google Scholar ↗
San Diego, CA, USA, 2013, pp. 160–168Google Scholar ↗
Taylor, S.J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.Google Scholar ↗

[refR-1] Ministry of Economy and Planning, "Vision 2030 and Economic Development in Saudi Arabia," Saudi Government Publications, 2020.Google Scholar ↗

[refR-2] J. Chae, J. Chen, T. Zhang, et al., "Social Media Analytics for Regional Trend Analysis," Journal of Business Research, vol. 112, pp. 125-137, 2020.Google Scholar ↗

[refR-3] Alshamsi, A. Qasmi, and A. Mohtaseb, "Consumer Sentiment Analysis for Saudi Arabia Using Social Media Data," Saudi Economic Review, vol. 11, no. 4, pp. 37-54, 2022.Google Scholar ↗

[refR-4] K. Aalijah and R. Irfan, "Scalable Taxonomy Generation and Evolution on Apache Spark," 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Calgary, AB, Canada, 2020, pp. 634-639, doi: 10.1109/DASC-PICom-CBDCom-DOI ↗Google Scholar ↗

[refR-5] CyberSciTech49142.2020.00110.Google Scholar ↗

[refR-6] M. Hussain, M. Alharthi, and M. Hawsawi, "AI and Social Media Trend Analysis in the Middle East," Journal of Middle Eastern Studies, vol. 24, no. 2, pp. 145-162, 2023.xGoogle Scholar ↗

[refR-7] Aalijah, K., Irfan, R., Umar, U., & Nayab, S. (2022). Automatic taxonomy generation and incremental evolution on Apache Spark Parallelization Framework. KIET Journal of Computing and Information Sciences, 5(1), 15–36.Google Scholar ↗

[refR-8] H. Chen, R. Chiang, and V. C. Storey, "Business Intelligence and Analytics: From Big Data to Big Impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, Dec. 2012.Google Scholar ↗

[refR-9] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, 2018.Google Scholar ↗

[refR-10] W. Antoun, R. Baly, and H. Hajj, "AraBERT: Transformer-based Model for Arabic Language Understanding," Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, pp. 9-15, 2020.Google Scholar ↗

[refR-11] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, 2018Google Scholar ↗

[refR-12] Y. Zhang, S. Wu, and K. Liang, "AI-Driven Consumer Insights for Emerging Markets," Technology and Society, vol. 37, pp. 293-306, 2020.Google Scholar ↗

[refR-13] Alshamsi, A. Qasmi, and A. Mohtaseb, "Consumer Sentiment Analysis for Saudi Arabia Using Social Media Data," Saudi Economic Review, vol. 11, no. 4, pp. 37-54, 2022.Google Scholar ↗

[refR-14] D. Jiang, C. Lu, and Y. Sun, "Multimodal Sentiment Analysis on Social Media Data," IEEE Transactions on Multimedia, vol. 23, no. 1, pp. 280-289, 2021.Google Scholar ↗

[refR-15] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016, pp. 164-166.Google Scholar ↗

[refR-16] I. Loshchilov and F. Hutter, "Decoupled Weight Decay Regularization," in Proc. Int. Conf. Learning Representations (ICLR), Vancouver, Canada, Apr. 2019.Google Scholar ↗

[refR-17] M. Grootendorst, "BERTopic: Leveraging BERT embeddings for topic modelling," arXiv preprint, 2020.Google Scholar ↗

[refR-18] M. Grootendorst, "BERTopic: Neural topic modelling with a class- based TF-IDF procedure," arXiv preprint arXiv:2203.05794, 2022Google Scholar ↗

[refR-19] R. J. G. B. Campello, D. Moulavi, and J. Sander, "Density-Based Clustering Based on Hierarchical Density Estimates," in Proc. of the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining,Google Scholar ↗

[refR-20] San Diego, CA, USA, 2013, pp. 160–168Google Scholar ↗

[refR-21] Taylor, S.J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.Google Scholar ↗