In this digital age, social media plays a pivotal role in instant information propagation across the world. However, the same characteristics also make the social media the major contributor to spreading misinformation. This paper contributes to the ongoing efforts in combating misinformation on social media (specially Twitter) platforms by adopting a holistic approach that combines network analysis, Machine Learning (ML), and psychological insights. It offers a detailed analysis of the Twitter network by exploring the dynamics of misinformation on Twitter. The resharing user and tweet network, as well as tweet and user features like tweet sentiment, user engagements, and their implications on misinformation propagation have been considered and evaluated. Extreme Gradient Boosting (XGBoost) achieved Mean Squared Error (MSE) of 26.81 and R2 score of 0.97 showing high predictive accuracy with relatively small prediction errors for verifying tweet authenticity. This can support the analysis of finding influential users and tweets in order to track and foster and improved understand of misinformation propagation. As the digital landscape continues to evolve, the strategies developed through this research will be instrumental in enhancing the integrity and reliability of information on social media, thereby fostering a healthier digital public discourse.

[1] Aggarwal T., NetworkX: A Comprehensive Guide to Mastering Network Analysis with Python, https://medium.com/@tushar_aggarwal/networkx-a- comprehensive-guide-to-mastering-network-analysis- with-python-fd7e5195f6a0, Last Visited, 2025.

[2] Aimeur E., Amri S., and Brassard G., “Fake News, Disinformation and Misinformation in Social Media: A Review,” Social Network Analysis and Mining, vol. 13, pp. 1-36, 2023. https://doi.org/10.1007/s13278-023-01028-5

[3] Arranz-Escudero O., Quijano-Sanchez L., and Liberatore F., “Enhancing Misinformation Countermeasures: A Multimodal Approach to Twitter Bot Detection,” Social Network Analysis and Mining, vol. 15, pp. 1-22, 2025. https://link.springer.com/article/10.1007/s13278- 025-01435-w

[4] Barrett P., Hunter J., Miller J., Hsu J., and Greenfield P., “Matplotlib a Portable Python Plotting Package,” in Proceedings of the 14th Astronomical Data Analysis Software and Systems Conference, Pasadena, pp. 91-95, 2004. file:///C:/Users/user/Downloads/matplotlib_-- _A_Portable_Python_Plotting_Package%20(1).pdf

[5] Bento A., Cruz C., Fernandes G., and Ferreira L., Unraveling the Dynamics of Fake News and Misinformation on Twitter: A Comprehensive Exploration 677 “Social Network Analysis: Applications and New Metrics for Supply Chain Management a Literature Review,” Logistics, vol. 8, no. 1, pp. 1- 20, 2024. https://doi.org/10.3390/logistics8010015

[6] Brownlee J., XGBoost for Regression, https://machinelearningmastery.com/xgboost-for- regression/, Last Visited, 2025.

[7] Cano-Marin E., Mora-Cantallops M., and Sanchez-Alonso S., “The Power of Big Data Analytics over Fake News: A Scientometric Review of Twitter as a Predictive System in Healthcare,” Technological Forecasting and Social Change, vol. 190, pp. 1-14, 2023. https://doi.org/10.1016/j.techfore.2023.122386

[8] Chaudhary M., F-IDF Vectorizer Scikit-Learn, https://medium.com/@cmukesh8688/tf-idf- vectorizer-scikit-learn-dbc0244a911a, Last Visited, 2025.

[9] Derr A., Network Centrality Infographic-Visible Network Labs, https://visiblenetworklabs.com/2021/04/16/under standing-network-centrality/, Last Visited, 2025.

[10] Dey P., Bhattacharya S., and Roy S., “A Survey on the Role of Centrality as Seed Nodes for Information Propagation in Large Scale Network,” ACM/IMS Transactions on Data Science, vol. 2, no. 3, pp. 1-25, 2021. https://doi.org/10.1145/3465374

[11] Duzen Z., Riveni M., and Aktas M., “Analyzing the Spread of Misinformation on Social Networks: A Process and Software Architecture for Detection and Analysis,” Computers, vol. 12, no. 11, pp. 1-17, 2023. https://doi.org/10.3390/computers12110232

[12] Ecker U., Lewandowsky S., Cook J., Schmid P., and et al., “The Psychological Drivers of Misinformation Belief and its Resistance to Correction,” Nature Reviews Psychology, vol. 1, pp. 13-29, 2022. https://doi.org/10.1038/s44159- 021-00006-y

[13] Frost J., How to Interpret R-Squared in Regression Analysis, https://statisticsbyjim.com/regression/interpret-r- squared-regression/, Last Visited, 2025.

[14] Frost J., Mean Squared Error (MSE), https://statisticsbyjim.com/regression/mean- squared-error-mse/, Last Visited, 2025.

[15] Gbaje E., Agwu C., Odigie I., and Yani S., “Curtailing Fake News Creation and Dissemination in Nigeria: Twitter Social Network and Sentiment Analysis Approaches,” Journal of Information Science, vol. 51, no. 4, pp. 987-999, 2025. https://doi.org/10.1177/01655515231160029

[16] GCFLearnFree.org, Digital Media Literacy, What is an Echo Chamber?, https://www.youtube.com/playlist?list=PLpQQip Wcxwt9NUsBX4KpO4PwHMilgzEh1, Last Visited, 2025.

[17] Gwiazdzinski P., Gundersen A., Piksa M., Krysinska I., and et al., “Psychological Interventions Countering Misinformation in Social Media: A Scoping Review,” Frontiers in Psychiatry, vol. 13, pp. 1-12, 2023. DOI: 3389/fpsyt.2022.974782

[18] Jahng M., “Is Fake News the New Social Media Crisis? Examining the Public Evaluation of Crisis Management for Corporate Organizations Targeted in Fake News,” Journal of Strategic Communication, vol. 15, no. 1, pp. 18-36, 2021. https://doi.org/10.1080/1553118X.2020.1848842

[19] Jensen K., Tweets and User Engagement, https://www.kaggle.com/datasets/thedevastator/tw eets-and-user-engagement/data, Last Visited, 2025.

[20] Johary, Liora Formerly DataScientist, Feature Engineering: Importance for Machine Learning, https://liora.io/en/feature-engineering- importance-for-machine-learning, Last Visited, 2025.

[21] Kanade V., What is a Support Vector Machine? Working, Types, and Examples, https://www.spiceworks.com/tech/big- data/articles/what-is-support-vector-machine/, Last Visited, 2025.

[22] Kansara P. and Adhvaryu K., “A Survey of Fake Data or Misinformation Detection Techniques Using Big Data and Sentiment Analysis,” SN Computer Science, vol. 5, pp. 955, 2024. https://doi.org/10.1007/s42979-024-03297-z

[23] Karami M., Nazer T., and Liu H., “Profiling Fake News Spreaders on Social Media Through Psychological and Motivational Factors,” in Proceedings of the 32nd ACM Conference on Hypertext and Social Media, London, pp. 225- 230, 2021. https://doi.org/10.1145/3465336.3475097

[24] Kwao L., Yang Y., Zou J., and Ma J., “A Survey of Approaches to Early Rumor Detection on Microblogging Platforms: Computational and Socio-Psychological Insights,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 15, no. 1, pp. e70001, 2025. https://doi.org/10.1002/widm.70001

[25] Mcleod S., Social Identity Theory in Psychology, https://www.simplypsychology.org/social- identity-theory.html, Last Visited, 2025.

[26] Metzler H. and Garcia D., “Social Drivers and Algorithmic Mechanisms on Digital Media,” Perspectives on Psychological Science, vol. 19, no. 5, pp. 735-748, 2024. https://doi.org/10.1177/17456916231185057

[27] Muhammed S. and Mathew S., “The Disaster of Misinformation: A Review of Research in Social Media,” International Journal of Data Science and Analytics, vol. 13, no. 4, pp. 271-285, 2022. 678 The International Arab Journal of Information Technology, Vol. 23, No. 4, July 2026 https://link.springer.com/article/10.1007/s41060- 022-00311-6

[28] Munoz P., Diez F., and Bellogin A., “Modeling Disinformation Networks on Twitter: Structure, Behavior, and Impact,” Applied Network Science, vol. 9, no. 4, pp. 1-35, 2024. https://link.springer.com/article/10.1007/s41109- 024-00610-w

[29] Munusamy S., Syasyila K., Shaari A., Pitchan M., and et al., “Psychological Factors Contributing to the Creation and Dissemination of Fake News Among Social Media Users: A Systematic Review,” BMC Psychology, vol. 12, pp. 1-14, 2024. https://doi.org/10.1186/s40359-024-02129-2

[30] Oroh A., Bandung Y., and Zagi L., “Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media,” in Proceedings of the IEEE Asia Pacific Conference on Wireless and Mobile, Bandung, pp. 206-212, 2021. DOI:10.1109/APWiMob51111.2021.9435268

[31] Park C., “Why People Rely on Fact-Checkers? Testing Theses of “Perceived Severity of Fake News” and “Disappointment in News Media”,” Journalism Studies, vol. 25, no. 1, pp. 1-18, 2024. https://doi.org/10.1080/1461670X.2023.2289878

[32] Pennycook G. and Rand D., “The Psychology of Fake News,” Trends in Cognitive Sciences, vol. 25, no. 5, pp. 388-402, 2021. https://doi.org/10.1016/j.tics.2021.02.007

[33] Raponi S., Khalifa Z., Oligeri G., and Di Pietro R., “Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights,” ACM Transactions on the Web, vol. 16, no. 3, pp. 1-34, 2022. https://dl.acm.org/doi/10.1145/3522756

[34] Rathje S. and Van Bavel J., “The Psychology of Virality,” Trends in Cognitive Sciences, vol. 29, no. 10, pp. 914-927, 2025. https://doi.org/10.1016/j.tics.2025.06.014

[35] Saxena A., Saxena P., and Reddy H., Fake News Propagation: A Review of Epidemic Models, Springer, 2022. https://link.springer.com/chapter/10.1007/978- 981-16-3398-0_16

[36] Sharma S. and Sharma R., “Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach,” in Proceedings of the International Joint Conference on Neural Networks, Shenzhen, pp. 1-8, 2021. https://doi.org/10.1109/IJCNN52387.2021.9534185

[37] Singh D. and Choudhury P., “Community Detection in Large-Scale Real-World Networks,” Advances in Computers, vol. 128, pp. 329-352, 2023. https://doi.org/10.1016/bs.adcom.2021.10.007

[38] Sitio C., Sibaroni Y., and Prasetiyowati S., “Identifying Possible Rumor Spreaders on Twitter Using the SVM and Feature Level Extraction,” Jurnal Teknik Informatika, vol. 4, no. 3, pp. 611- 618, 2023. https://doi.org/10.52436/1.jutif.2023.4.3.868

[39] Smith S., Kao E., Mackin E., Shah D., and et al., “Automatic Detection of Influential Actors in Disinformation Networks,” Proceedings of the National Academy of Sciences, vol. 118, no. 4, pp. 1-10, 2021. https://doi.org/10.1073/pnas.2011216118

[40] Soman A., “Fake News Detection Using Machine Learning Method,” International Journal of Advance Research and Innovative Ideas in Education, vol. 8, no. 3, pp. 4044-4049, 2022. https://ijariie.com/manuscript/17300

[41] Srebrenik B., Fake News Detection Through Ego Network Analysis, https://github.com/briansrebrenik/Final_Project, Last Visited, 2025.

[42] Thelwall M., Mozdeh, GitHub, https://github.com/MikeThelwall/Mozdeh, Last Visited, 2025.

[43] Tommasel A., Rodriguez J., and Menczer F., “Following the Trail of Fake News Spreaders in Social Media: A Deep Learning Model,” in Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Barcelona, pp. 29-34, 2022. https://dl.acm.org/doi/10.1145/3511047.3536410

[44] Ul Hussna A., Alam M., Islam R., Alkhamees B., and et al., “Dissecting the Infodemic: An In-Depth Analysis of COVID-19 Misinformation Detection on X (Formerly Twitter) Utilizing Machine Learning and Deep Learning Techniques,” Heliyon, vol. 10, no. 18, pp. 1-22, 2024. https://doi.org/10.1016/j.heliyon.2024.e37760

[45] Wankhade M., Rao A., and Kulkarni C., “A Survey on Sentiment Analysis Methods, Applications, and Challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731-5780, 2022. https://doi.org/10.1007/s10462-022-10144-1