-Abedi, M. M., & Sacchi, E. (2024). A machine learning tool for collecting and analyzing subjective road safety data from Twitter. Expert Systems with Applications, 240, 122582.
-Aggarwal, C. C., & Reddy, C. K. (2013). Data Clustering: Algorithms and Applications. Chapman & Hall/CRC.
-Ali, F., Ali, A., Imran, M., Naqvi, R. A., Siddiqi, M. H., & Kwak, K.-S. (2021). Traffic accident detection and condition analysis based on social networking data. Accident Analysis & Prevention, 151, 105973.
-Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization,
-Chakraborty, P., & Sharma, A. (2019). Public opinion analysis of the transportation policy using social media data: a case study on the Delhi odd–even policy. Transportation in Developing Economies, 5, 1–9.
-Chang, H., Li, L., Huang, J., Zhang, Q., & Chin, K.-S. (2022). Tracking traffic congestion and accidents using social media data: A case study of Shanghai. Accident Analysis & Prevention, 169, 106618.
-Chen, X., Wang, Z., & Di, X. (2023). Sentiment analysis on multimodal transportation during the covid-19 using social media data. Information, 14(2), 113.
-Das, S., & Zubaidi, H. A. (2023). City transit rider tweets: understanding sentiments and politeness. Journal of Urban Technology, 30(1), 111–126.
-Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, 36, 10088–10115.
-Devlin, J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
-Efthymiou, D., & Antoniou, C. (2012). Use of social media for transport data collection. Procedia-Social and Behavioral Sciences, 48, 775–785.
-Evans-Cowley, J. S., & Griffin, G. (2012). Microparticipation with social media for community engagement in transportation planning. Transportation Research Record, 2307(1), 90–98.
-González-Ibáñez, R., Muresan, S., & Wacholder, N. (2011, June). Identifying Sarcasm in Twitter: A Closer Look. In D. Lin, Y. Matsumoto, & R. Mihalcea, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies Portland, Oregon, USA.
-Grant‐Muller, S. M., Gal‐Tzur, A., Minkov, E., Nocera, S., Kuflik, T., & Shoor, I. (2015). Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data. IET Intelligent Transport Systems, 9(4), 407–417.
-Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). Lora: Low-rank adaptation of large language models. ICLR, 1(2), 3.
ISPA. (2025). Iranians' usage rate of messaging apps and social networks.
-Japkowicz, N. (2013). Assessment metrics for imbalanced learning. Imbalanced learning: Foundations, algorithms, and applications,
187–206.
-Kamga, C., Kish, R., Mudigonda, S., & Tchamna, R. (2023). Utilizing Social Media Data for Estimating Transit Performance Metrics. Sustainability, 15(23), 16183.
-Kuflik, T., Minkov, E., Nocera, S.,
Grant-Muller, S., Gal-Tzur, A., & Shoor, I. (2017). Automating a framework to extract and analyse transport related social media content: The potential and the challenges. Transportation Research Part C: Emerging Technologies, 77, 275–291.
-Li, D., Zhang, Y., & Li, C. (2019). Mining public opinion on transportation systems based on social media data. Sustainability, 11(15), 4016.
-Liao, Y., Guo, H., & Shi, H. (2024). Research on the Public’s Intention to Use Shared Autonomous Vehicles: Based on Social Media Data Mining and Questionnaire Survey. Sustainability, 16(11), 4462.
-Liu, J., Shang, Z., Ke, W., Wang, P., Luo, Z., Liu, J., Li, G., & Li, Y. (2025, July). LLM-Guided Semantic-Aware Clustering for Topic Modeling. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Vienna, Austria.
-Lock, O., & Pettit, C. (2020). Social media as passive geo-participation in transportation planning–how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Geo-Spatial Information Science, 23(4), 275–292.
-Manik, M. M. H., & Wang, G. (2026). Gemma 4, Phi-4, and Qwen3: Accuracy-Efficiency Tradeoffs in Dense and MoE Reasoning Language Models. arXiv preprint arXiv:2604.07035.
-Mathur, S., Ninan, J., Vuorinen, L., Ke, Y., & Sankaran, S. (2021). An exploratory study of the use of social media to assess benefits realization in transport infrastructure projects. Project Leadership and Society, 2, 100010.
-Méndez, J. T., Lobel, H., Parra, D., & Herrera, J. C. (2019). Using Twitter to infer user satisfaction with public transport: the case of Santiago, Chile. IEEE Access, 7, 60255–60263.
-Montes, C. M., Feldt, R., Martos, C. M., Ouhbi, S., Premanandan, S., & Graziotin, D. (2025). Large Language Models in Thematic Analysis: Prompt Engineering, Evaluation, and Guidelines for Qualitative Software Engineering Research. arXiv preprint arXiv:2510.18456.
-Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
Richardson, L. (2007). Beautiful soup documentation. In: April.
-Ruan, K., Wang, X., & Di, X. (2024). From twitter to reasoner: Understand mobility travel modes and sentiment using large language models. 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC),
-Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. Proceedings of the 19th international conference on World wide web,
-Saragih, M. H., & Girsang, A. S. (2017). Sentiment analysis of customer engagement on social media in transport online. 2017 International Conference on Sustainable Information Engineering and Technology (SIET),
-Serna, A., Soroa, A., & Agerri, R. (2021). Applying deep learning techniques for sentiment analysis to assess sustainable transport. Sustainability, 13(4), 2397.
-Sujon, M., & Dai, F. (2021). Social media mining for understanding traffic safety culture in washington state using twitter data. Journal of Computing in Civil Engineering, 35(1), 04020059.
-Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., & Azhar, F. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
-Wang, S., Zhao, Z., Xie, Y., Ma, M., Chen, Z., Wang, Z., Su, B., Xu, W., & Li, T. (2024). Recent surge in public interest in transportation: Sentiment analysis of Baidu Apollo Go using Weibo data. arXiv preprint arXiv:2408.10088.
-Wojtowicz, J., & Wallace, W. A. (2016). Use of social media by transportation agencies for traffic management. Transportation Research Record, 2551(1), 82–89.
