Utilizing Artificial Intelligence Techniques in Big Data Analysis of Social Media Users' Sentiments towards World Cup 2022
DOI:
https://doi.org/10.35516/Hum.2026.8995Keywords:
Artificial Intelligence, social media analysis, sentiment analysis, topic modeling, big data, Qatar World Cup 2022Abstract
Objectives: The study aimed to explore the use of artificial intelligence (AI) techniques in analyzing big data on social networks concerning the 2022 FIFA World Cup, focusing on the platform X (formerly Twitter). This was achieved using a survey methodology and social network analysis tools.
Methods: The study provided a comprehensive analysis of sentiments and topics discussed on the X platform during three time periods: before the World Cup, during the event, and after the tournament. A massive dataset exceeding 8.5 million tweets was collected using eight hashtags related to the global sporting event. The data was analyzed with advanced AI algorithms and machine learning techniques, including sentiment analysis and topic modeling.
Results: The findings revealed a significant positive shift of 43.87% from the first to the third period of the study, accompanied by a corresponding decline in negative sentiments by -13.51%. These shifts highlight the successful execution of the World Cup and increased satisfaction and positive perceptions among users, as evidenced by the rise in positive sentiments and the decline in negative ones regarding the World Cup as a whole.
Conclusion: Big data was analyzed with high efficiency and accuracy to uncover shifts in sentiments and topics discussed during the World Cup. Additionally, the study evaluated the role of Qatar's hosting of the event and its impact on global sentiments. This prominent event led to notable changes in emotional responses toward Arabs, with particular emphasis on the host country of the 2022 FIFA World Cup.
Downloads
References
Abdel Azim, T. (2019). Measuring students' perception of the role of Saudi media in raising awareness of local cultural heritage. Journal of Humanities and Social Sciences, University of Hail, 3(8).
AbdelAziz, F. M. (2023). Political employment of hate speech in sports coverage: Case study of Qatar 2022 World Cup. Journal of Mass Communication Research, 64, 1697-1724.
Abdul Mohsen, M. (2023). Processing infographic journalism on online websites for sports events: A case study of the 2022 FIFA World Cup. Beni Suef Journal of Education and Sports Sciences, 6(11).
Al-Badawi, T. (2019). New media theories (1st ed.). Al-Rushd Publishing and Distribution.
Al-Hout, M. (2023). Coverage of Al Jazeera News Channel on the 2023 World Cup: An analytical study (Unpublished Master's thesis). Middle East University, Amman, Jordan.
Al-Shahri, M. (2024). Ethics of public discourse in Saudi social media regarding prominent issues (Unpublished PhD dissertation). Imam Muhammad bin Saud Islamic University, Riyadh, Saudi Arabia.
Azzalini, A., & Scarpa, B. (2012). Data analysis and data mining: An introduction. Oxford University Press.
Barbieri, F., Anke, L. E., Camacho-Collados, J., & et al. (2022). XLM-T: Multilingual language models in Twitter for sentiment analysis and beyond. Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France.
Barbieri, F., Camacho-Collados, J., Neves, L., & et al. (2020). TWEETEVAL: Unified benchmark and comparative evaluation for tweet classification. Findings of the Association for Computational Linguistics: EMNLP 2020, 1644–1650. Association for Computational Linguistics.
Barbieri, F., Camacho-Collados, J., Neves, L., & et al. (2020). TWEETEVAL: Unified benchmark and comparative evaluation for tweet classification. Association for Computational Linguistics, Findings of the Association for Computational Linguistics: EMNLP 2020, 1644–1650.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Brannagan, P. M., & Reiche, D. (2022). The controversial games: Responses to Qatar’s 2022 World Cup. In Qatar and the 2022 FIFA World Cup (pp. xx-xx). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-96822-9_4
Devlin, J., Chang, M., Lee, K., & et al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota.
Faisal, A., & Hamdi, M. (2023). The French image and the 2022 World Cup in Qatar: An analytical reading of a sample of French newspapers. Arab Future Magazine, 529.
Grootendorst, M. (2022). BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics. arXiv preprint arXiv:2007.03788.
Haddad, A. (2022). Coverage of the New York Times website on the Qatar 2022 World Cup. Egyptian Journal of Public Opinion Research, No. 2, Part 2.
Inoue, G., Alhafni, B., Baimukan, N., & et al. (2021). The interplay of variant, size, and task type in Arabic pre-trained language models. WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Kyiv, Ukraine.
Kumar, S., Morstatter, F., & Liu, H. (2014). Twitter data analytics. Springer.
Kwak, H., Lee, C., Park, H., & et al. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web (WWW '10). Association for Computing Machinery, New York, NY, USA.
Naskar, D., Mokaddem, S., Rebollo, M., & et al. (2016). Sentiment analysis in social networks through topic modeling. In 10th Conference on International Language Resources and Evaluation (LREC'16), Valencia, Spain.
Nisyah, C., & Sayuti, S. A. (2023). Formation of a positive image through FIFA policy in Qatar 2022 on online news media (Model Teun A Van Dijk). International Journal of Multidisciplinary Research and Analysis, 6(8), 3484-3489.
Refaat, M. (2018). Public opinion in virtual reality and the power of virtual mobilization (1st ed.). Al-Arabi Publishing and Distribution.
Sawwan, F. (2016). Scientific research – Concepts – ideas – methods and processes (1st ed.). Ibn Al-Nazir Publishing and Distribution.
Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science & artificial intelligence: Systems for decision support. Pearson Education, Inc.
Siddiqui, S., & Singh, T. (2016). Social media: Its impact with positive and negative aspects. International Journal of Computer Applications Technology and Research, 5(2), 71-75.
Van Der Hulst, R. C. (2019). Introduction to network analysis (SNA) as an investigative tool. Springer Science and Business Media, 12, 104-105.
Vaswani, A., Shazeer, N., Parmar, N., & et al. (2017). Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
Zaki, W. (2015). Social network theory: From ideology to methodology. Arab Forum for Social and Human Sciences. Retrieved from https://socio.yoo7.com/t3886-topic
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dirasat: Human and Social Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2024-12-09
Published 2026-01-01


