Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution

Document Type : Original Article

Authors

1 Department of Computer Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran

2 Shahrood University of Technology

Abstract

The rapid growth of Twitter has transformed it into a critical real-time sensor for world events, often surfacing information about disasters, political upheaval, and public health crises ahead of traditional sources. While detecting major events is valuable, the ability to identify sub-events—fine-grained, evolving components—is crucial for deeper situational awareness. This survey provides a comprehensive review of NLP techniques for sub-event prediction and evolution on Twitter. We introduce a novel taxonomy that categorizes methods from traditional text-based and graph-based approaches to modern deep learning and transformer-based architectures, specifically evaluating their capacity to capture sub-event dynamics. Our analysis covers widely used benchmarks (e.g., CrisisNLP, CrisisBench, COVID-Twitter datasets) and evaluation protocols (e.g., precision, recall, F1, clustering metrics). The findings indicate that while significant advances have been made, the fusion of multimodal data, the application of large language models, and the adoption of privacy-preserving frameworks like federated learning represent the most promising pathways for robust sub-event detection. By synthesizing methodological advances and evaluation practices, this paper underscores the central role of sub-event analysis in advancing research and its critical importance for real-time, high-stakes applications in disaster response, public health, and security.

Keywords

References

  1. Atefeh, F., & Khreich, W. (2015). A survey of techniques for event detection in Twitter. Computational Intelligence, 31(1), 133–164. doi:10.1111/coin.12017.
  2. Li, Q., Chao, Y., Li, D., Lu, Y., & Zhang, C. (2022). Event Detection from Social Media Stream: Methods, Datasets and Opportunities. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 3509–3516. doi:10.1109/BigData55660.2022.10020411.
  3. Mredula, M. S., Dey, N., Rahman, M. S., Mahmud, I., & Cho, Y. Z. (2022). A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data. Sensors, 22(12), 4531. doi:10.3390/s22124531.
  4. Parekh, T., Mac, A., Yu, J., Dong, Y., Shahriar, S., Liu, B., Yang, E., Huang, K. H., Wang, W., Peng, N., & Chang, K. W. (2024). Event Detection from Social Media for Epidemic Prediction. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024, 1, 5758–5783. doi:10.18653/v1/2024.naacl-long.322.
  5. Belcastro, L., Marozzo, F., Talia, D., Trunfio, P., Branda, F., Palpanas, T., & Imran, M. (2021). Using social media for sub-event detection during disasters. Journal of Big Data, 8(1). doi:10.1186/s40537-021-00467-1.
  6. Rajaby Faghihi, H., Alhafni, B., Zhang, K., Ran, S., Tetreault, J., & Jaimes, A. (2022). CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization. Findings of the Association for Computational Linguistics: EMNLP 2022, 5455–5477. doi:10.18653/v1/2022.findings-emnlp.400.
  7. Senthilkumar, K. K., Bhatt, C., Renuka Jyothi, S., Patil, S. B., Ravivarman, G., & Mahajan, S. (2024). Twitter Sarcasm Detection using Natural Language Processing and Deep Learning Techniques. 2024 Global Conference on Communications and Information Technologies, GCCIT 2024. doi:10.1109/GCCIT63234.2024.10862514.
  8. Narasamma, V. L., & Sreedevi, M. (2021). Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework. International Journal of Advanced Computer Science and Applications, 12(5), 440–447. doi:10.14569/IJACSA.2021.0120555.
  9. Kolajo, T., Daramola, O., & Adebiyi, A. A. (2022). Real-time event detection in social media streams through semantic analysis of noisy terms. Journal of Big Data, 9(1). doi:10.1186/s40537-022-00642-y.
  10. Fedoryszak, M., Frederick, B., Rajaram, V., & Zhong, C. (2019). Real-time Event Detection on Social Data Streams. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2774–2782. doi:10.1145/3292500.3330689.
  11. Thilak Raj, M., Nivetha, R., Nithish Kumar, S., & Varshini, C. (2024). Cyber Sleuth: Harnessing NLP and Blockchain for Twitter Based Fake News Detection. 2nd International Conference on Artificial Intelligence and Machine Learning Applications: Healthcare and Internet of Things, AIMLA 2024. doi:10.1109/AIMLA59606.2024.10531313.
  12. Dahlan, F., & Suyanto, S. (2023). Data Augmentations to Improve BERT-based Detection of Covid-19 Fake News on Twitter. 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), 140–145. doi:10.1109/iccosite57641.2023.10127796.
  13. Cui, J. (2025). Enhancing Security Event Detection on Twitter with Graph-based Tweet Embedding. Network and Distributed System Security (NDSS) Symposium Symposium. doi:10.14722/aiscc.2024.23002.
  14. -, A. S., -, D. K., & -, A. G. (2024). AI-Enhanced Cyberbullying Detection in Encrypted Social Media: A Privacy-Preserving Federated Learning Approach. International Journal on Science and Technology, 15(2). doi:10.71097/ijsat.v15.i2.4011.
  15. Cai, Z., Kung, P.-N., Suvarna, A., Ma, M., Bansal, H., Chang, B., . . . Peng, N. (2024, August). Improving Event Definition Following For Zero-Shot Event Detection. In L.-W. Ku, A. Martins, & V. Srikumar (Ed.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2842–2863). Bangkok: Association for Computational Linguistics. doi:10.18653/v1/2024.acl-long.157
  16. Pouran Ben Veyseh, A., Lai, V., Dernoncourt, F., & Nguyen, T. H. (2021). Unleash GPT-2 Power for Event Detection. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 1, 6271–6282. doi:18653/v1/2021.acl-long.490.
  17. Balci, E., & Sarac, E. (2024). Automated Depression Detection from Tweets: A Comparison of NLP Techniques. 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024. doi:10.1109/IDAP64064.2024.10711029.
  18. ALAMSYAH, N., Saparudin, & Prima Kurniati, A. (2024). Event Detection Optimization Through Stacking Ensemble and BERT Fine-Tuning for Dynamic Pricing of Airline Tickets. IEEE Access, 12, 145254–145269. doi:10.1109/ACCESS.2024.3466270.
  19. Akintoye, O., Wei, N., & Liu, Q. (2024). Suicide Detection in Tweets Using LSTM and Transformers. Proceedings - 2024 4th Asia Conference on Information Engineering, ACIE 2024, 22–27. doi:10.1109/ACIE61839.2024.00011.
  20. Qiu, Z., Wu, J., Yang, J., Su, X., & Aggarwal, C. (2025). Heterogeneous Social Event Detection via Hyperbolic Graph Representations. IEEE Transactions on Big Data, 11(1), 115–129. doi:10.1109/TBDATA.2024.3381017.
  21. El-Niss, A., Alzu’Bi, A., & Abuarqoub, A. (2023). Multimodal Fusion for Disaster Event Classification on Social Media: A Deep Federated Learning Approach. Proceedings of the 7th International Conference on Future Networks and Distributed Systems, 758–763. doi:10.1145/3644713.3644840.
  22. Vasconcelos, A. B., Drummond, L. M. d. A., Brum, R. C., & Paes, A. (2023). Exploring Federated Learning to Trace Depression in Social Media with Language Models. 2023 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW), 24–30. doi:10.1109/sbac-padw60351.2023.00014.
  23. Healy, P., Hunt, G., Kilroy, S., Lynn, T., Morrison, J. P., & Venkatagiri, S. (2015). Evaluation of peak detection algorithms for social media event detection. 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 1–9. doi:10.1109/smap.2015.7370090.
  24. Malik, M., Aslam, W., Aslam, Z., Alharbi, A., Alouffi, B., & Rauf, H. T. (2022). A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets. Computational Intelligence and Neuroscience, 2022. doi:10.1155/2022/5980043.
  25. Zhang, B., Yang, Y., Niu, F., Fu, X., Dai, G., & Huang, H. (2025, November). SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Ed.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 23061–23073). Suzhou: Association for Computational Linguistics. doi:10.18653/v1/2025.emnlp-main.1176
  26. Sasaki, K., Yoshikawa, T., & Furuhashi, T. (2014). Online topic model for Twitter considering dynamics of user interests and topic trends. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1977–1985. doi:10.3115/v1/d14-1212.
  27. Lim, K. W., & Buntine, W. (2014). Twitter Opinion Topic Model. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 1319–1328. doi:10.1145/2661829.2662005.
  28. Lossio-Ventura, J. A., Gonzales, S., Morzan, J., Alatrista-Salas, H., Hernandez-Boussard, T., & Bian, J. (2021). Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine, 117, 102096. doi:10.1016/j.artmed.2021.102096.
  29. Lossio-Ventura, J. A., Morzan, J., Alatrista-Salas, H., Hernandez-Boussard, T., & Bian, J. (2019). Clustering and topic modeling over tweets: A comparison over a health dataset. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1544–1547. doi:10.1109/bibm47256.2019.8983167.
  30. Ganeshkumar, P., BR, A. K., Padmanabhan, S., & others. (2022). Social Media Personal Event Notifier Using NLP and Deep Learning. 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), (pp. 1–5). DOI: 10.1109/ICPECTS56089.2022.10047710
  31. Desai, M., Mehta, R. G., & Rana, D. P. (2024). Anatomising the impact of ResearchGate followers and followings on influence identification. Journal of Information Science, 50(3), 607–624. doi:10.1177/01655515221100716.
  32. Singh, R.R. (2022). Centrality Measures: A Tool to Identify Key Actors in Social Networks. Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. doi:10.1007/978-981-16-3398-0_1.
  33. Romero, D. M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. Proceedings of the 20th International Conference on World Wide Web, WWW 2011, 695–704. doi:10.1145/1963405.1963503.
  34. Lin, Y., Yang, D., Hou, J., Yan, C., Kim, M., Laurienti, P. J., & Wu, G. (2021). Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks. NeuroImage, 230, 117791. doi:10.1016/j.neuroimage.2021.117791.
  35. Qiu, Z., Ma, C., Wu, J., & Yang, J. (2024). An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space. WWW 2024 - Proceedings of the ACM Web Conference, 2519–2529. doi:10.1145/3589334.3645526.
  36. Huang, W., Zong, Y., Shi, Z., & Liu, P. (2023). MESCAL: Malicious Login Detection Based on Heterogeneous Graph Embedding with Supervised Contrastive Learning. 2023 IEEE Symposium on Computers and Communications (ISCC), 1274–1279. doi:10.1109/iscc58397.2023.10218074.
  37. Cekinel, R. F., & Karagoz, P. (2022). Event prediction from news text using subgraph embedding and graph sequence mining. World Wide Web, 25(6), 2403–2428. doi:10.1007/s11280-021-01002-1.
  38. Kleinberg, J. (2003). Bursty and Hierarchical Structure in Streams. Data Mining and Knowledge Discovery, 7(4), 373–397. doi:10.1023/a:1024940629314.
  39. Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer, Berlin, Germany. doi:10.1007/978-3-540-27752-1.
  40. Jolliffe, I. (2025). Principal Component Analysis. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Germany. doi:10.1007/978-3-662-69359-9_483
  41. Seetha, A., Chouhan, S. S., Pilli, E. S., & Raychoudhury, V. (2024). DiEvD: Disruptive Event Detection from Dynamic Datastreams Using Continual Machine Learning: A Case Study with Twitter. IEEE Transactions on Emerging Topics in Computing, 12(3), 727–738. doi:10.1109/TETC.2023.3272973.
  42. Rashmi, C., Soumya, B., Harika, A., & Harathi, D. (2024). Live Event Detection for People’s Safety Using Nlp and Deep Learning. Turkish Journal of Computer and Mathematics Education (Turcomat), 15(3), 434–441. doi:10.61841/turcomat.v15i3.14956.
  43. Purnomo, A., Naufal, A. A., Yudha, E. P., & Arifin, A. Z. (2020). Tweet Classification Using Deep Learning Architecture for Concert Event Detection. Jurnal Ilmu Komputer Dan Informasi, 13(2), 57–63. doi:10.21609/jiki.v13i2.815.
  44. Fang, Y., Gao, J., Liu, Z., & Huang, C. (2020). Detecting cyber threat event from twitter using IDCNN and BiLSTM. Applied Sciences (Switzerland), 10(17), 5922. doi:10.3390/app10175922.
  45. Sen, A., Rajakumaran, G., Mahdal, M., Usharani, S., Rajasekharan, V., Vincent, R., & Sugavanan, K. (2024). Live Event Detection for People’s Safety Using NLP and Deep Learning. IEEE Access, 12, 6455-6472. doi:10.1109/ACCESS.2023.3349097
  46. Ghaswala, M., Gomes, C., Kumar, S. M., & Sweta, S. (2024). Depression Detection from social media Text Using NLP and Deep Learning Techniques. 2024 First International Conference for Women in Computing (InCoWoCo), 1–7. doi:10.1109/incowoco64194.2024.10863118.
  47. Adesokan, A., Madria, S., & Nguyen, L. (2023). HatEmoTweet: low-level emotion classifications and spatiotemporal trends of hate and offensive COVID-19 tweets. Social Network Analysis and Mining, 13(1). doi:10.1007/s13278-023-01132-6.
  48. Maveli, N. (2020). EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets. Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-NUT 2020, 455–461. doi:10.18653/v1/2020.wnut-1.67.
  49. Tran, K., Phan, H., Nguyen, K., & Thuy Nguyen, N. L. (2020). UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network. Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-NUT 2020, 383–387. doi:10.18653/v1/2020.wnut-1.53.
  50. Dey, A., Bothera, A., Sarikonda, S., Aryan, R., Podishetty, S. K., Havalgi, A., Srivastava, S., & Singh, G. (2026). Multimodal Event Detection: Current Approaches and Defining the New Playground Through LLMs and VLMs. Natural Language Processing and Information Systems. NLDB 2025. Lecture Notes in Computer Science, vol 15836. Springer, Cham, Switzerland. doi:10.1007/978-3-031-97141-9_31.
  51. Kashif, M., Zohair, M., & Ali, S. (2023). Lexical squad@ multimodal hate speech event detection 2023: Multimodal hate speech detection using fused ensemble approach. Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 7 September, 2023, Varna, Bulgaria.
  52. Jin, Y., Choi, M., Verma, G., Wang, J., & Kumar, S. (2024). MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms. Findings of the Association for Computational Linguistics ACL 2024, 6192–6210. doi:10.18653/v1/2024.findings-acl.370.
  53. Xu, J., Zhao, H., Liu, W., & Ding, X. (2023). Research on False Information Detection Based on Multimodal Event Memory Network. 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), 566–570. doi:10.1109/iccece58074.2023.10135191.
  54. Esackimuthu, S., & Balasundaram, P. (202). Verbavisor@ multimodal hate speech event detection 2023: Hate speech detection using transformer model. Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 7 September, 2023, Varna, Bulgaria.
  55. Alam, F., Sajjad, H., Imran, M., & Ofli, F. (2021). CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing. Proceedings of the International AAAI Conference on Web and Social Media, 15, 923–932. doi:10.1609/icwsm.v15i1.18115.
  56. Varachkina, H., Ziehe, S., Dönicke, T., & Pannach, F. (2020). #GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets. Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-NUT 2020, 462–465. doi:10.18653/v1/2020.wnut-1.68.
  57. Neruda, G. A., & Winarko, E. (2021). Traffic Event Detection from Twitter Using a Combination of CNN and BERT. 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021. doi:10.1109/ICACSIS53237.2021.9631334.
  58. Sech, J., DeLucia, A., Buczak, A. L., & Dredze, M. (2020, November). Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest. In W. Xu, A. Ritter, T. Baldwin, & A. Rahimi (Ed.), Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020) (pp. 215–221). Online: Association for Computational Linguistics. doi:10.18653/v1/2020.wnut-1.28
  59. Suppa, M., Skala, D., Jass, D., Sucik, S., Svec, A., & Hraska, P. (2024). Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA. Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text (CASE 2024), 166–177. doi:10.18653/v1/2024.case-1.23.
  60. Owusu-Adjei, M., Ben Hayfron-Acquah, J., Frimpong, T., & Abdul-Salaam, G. (2023). Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. PLOS Digital Health, 2(11), e0000290. doi:10.1371/journal.pdig.0000290.
  61. Munshi, M., Gupta, R., Jadav, N. K., Polkowski, Z., Tanwar, S., Alqahtani, F., & Said, W. (2024). Quantum machine learning-based framework to detect heart failures in Healthcare 4.0. Software - Practice and Experience, 54(2), 168–185. doi:10.1002/spe.3264.
  62. Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91. doi:10.18653/v1/2020.eval4nlp-1.9.
  63. Fox, A., Swarup, S., & Adiga, A. (2025, April). A Unifying Information-theoretic Perspective on Evaluating Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence, 39, 16630–16638. doi:10.1609/aaai.v39i16.33827
  64. Jiao, X., Wan, S., Liu, Q., Bi, Y., Lee, Y. L., Xu, E., Hao, D., & Zhou, T. (2024). Comparing discriminating abilities of evaluation metrics in link prediction. Journal of Physics: Complexity, 5(2), 25014. doi:10.1088/2632-072X/ad46be.
  65. Richardson, E., Trevizani, R., Greenbaum, J. A., Carter, H., Nielsen, M., & Peters, B. (2024). The receiver operating characteristic curve accurately assesses imbalanced datasets. Patterns, 5, 100994. doi:https://doi.org/10.1016/j.patter.2024.100994
  66. Perrella, S., Proietti, L., Huguet Cabot, P.-L., Barba, E., & Navigli, R. (2024). Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 20689–20714. doi:10.18653/v1/2024.emnlp-main.1152.
  67. Farruque, N., Goebel, R., Sivapalan, S., & Zaïane, O. R. (2024). Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach. Language Resources and Evaluation, 58(3), 1013–1041. doi:10.1007/s10579-024-09720-4.
  68. Ilham, F., & Maharani, W. (2022). Analyze Detection Depression In Social Media Twitter Using Bidirectional Encoder Representations from Transformers. Journal of Information System Research (JOSH), 3(4), 476–482. doi:10.47065/josh.v3i4.1885.
  69. Kostakos, P., Nykanen, M., Martinviita, M., Pandya, A., & Oussalah, M. (2018). Meta-Terrorism: Identifying Linguistic Patterns in Public Discourse After an Attack. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 1079–1083. doi:10.1109/asonam.2018.8508647.
  70. Williams, A. R., Burke-Moore, L., Chan, R. S.-Y., Enock, F. E., Nanni, F., Sippy, T., . . . Bright, J. (2025, March 17). Large language models can consistently generate high-quality content for election disinformation operations. PLOS ONE, 20, e0317421. doi:10.1371/journal.pone.0317421
  71. Salim, S., Turnbull, B., & Moustafa, N. (2024). A Blockchain-Enabled Explainable Federated Learning for Securing Internet-of-Things-Based Social Media 3.0 Networks. IEEE Transactions on Computational Social Systems, 11(4), 4681–4697. doi:10.1109/TCSS.2021.3134463.
  72. Sen, J., Waghela, H., & Rakshit, S. (2025). Privacy in Federated Learning. Data Privacy - Techniques, Applications, and Standards. IntechOpen Limited, London, United Kingdom. doi:10.5772/intechopen.1006677.
  73. Qiu, H., Dou, Z.-Y., Wang, T., Celikyilmaz, A., & Peng, N. (2023). Gender Biases in Automatic Evaluation Metrics for Image Captioning. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 8358–8375. doi:10.18653/v1/2023.emnlp-main.520.
  74. Chowdhury, S. R., Basu, S., & Maulik, U. (2022). A survey on event and subevent detection from microblog data towards crisis management. International Journal of Data Science and Analytics, 14(4), 319–349. doi:10.1007/s41060-022-00335-y.
  75. Nolasco, D., & Oliveira, J. (2019). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems, 93, 290–303. doi:10.1016/j.future.2018.09.008.
  76. Lu, G., Mu, Y., Gu, J., Kouassi, F. A. P., Lu, C., Wang, R., & Chen, A. (2021). A hashtag-based sub-event detection framework for social media. Computers and Electrical Engineering, 94, 107317. doi:10.1016/j.compeleceng.2021.107317.
  77. Xiao, K., Qian, Z., & Qin, B. (2022). A Survey of Data Representation for Multi-Modality Event Detection and Evolution. Applied Sciences (Switzerland), 12(4), 2204. doi:10.3390/app12042204.
  78. Koupaee, M., Bai, X., Chen, M., Durrett, G., Chambers, N., & Balasubramanian, N. (2025, July). Causal Graph based Event Reasoning using Semantic Relation Experts. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Ed.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 26169–26199). Vienna: Association for Computational Linguistics. doi:10.18653/v1/2025.acl-long.1269
  79. Chen, R., Qin, C., Jiang, W., & Choi, D. (2024, March). Is a Large Language Model a Good Annotator for Event Extraction? Proceedings of the AAAI Conference on Artificial Intelligence, 38, 17772–17780. doi:10.1609/aaai.v38i16.29730
  80. Dusart, A., Pinel-Sauvagnat, K., & Hubert, G. (2023). TSSuBERT: How to Sum Up Multiple Years of Reading in a Few Tweets. ACM Transactions on Information Systems, 41(4). doi:10.1145/3581786.
  81. Wang, Y., Su, Z., Pan, Y., Luan, T. H., Li, R., & Yu, S. (2024). Social-Aware Clustered Federated Learning With Customized Privacy Preservation. IEEE/ACM Transactions on Networking, 32(5), 3654–3668. doi:10.1109/TNET.2024.3379439.
  82. Pare, T., Haque, M. J., & Bhaladhare, P. R. (2024). Federated Learning Approach for Social Media Sentiment Analysis: Analyzing Public Opinion. 2024 8th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024. doi:10.1109/ICCUBEA61740.2024.10774886.
  83. Tarun Pare. (2024). Crowdsourced Intelligence: A Federated Learning Approach to Analyzing Public Opin-ion on Social Media. Journal of Electrical Systems, 20(10s), 7909–7920. doi:10.52783/jes.7006.
  84. Mistry, D., Plabon, J. D., Diba, B. S., Mukta, M. S. H., & Mridha, M. F. (2024). Federated Learning-Based Architecture for Personalized Next Emoji Prediction for Social Media Comments. IEEE Access, 12, 140339–140358. doi:10.1109/ACCESS.2024.3448470.
  85. Zhang, S. (2024). Using Federated Learning Technology to Build a Social Media User Model for China’s Multicultural Integration. 2024 International Conference on Language Technology and Digital Humanities (LTDH), 81–86. doi:10.1109/ltdh64262.2024.00025.
  86. Huang, W., Tiropanis, T., & Konstantinidis, G. (2023). A Dual-Layer Privacy-Preserving Federated Learning Framework. Web Information Systems Engineering – WISE 2023: 24th International Conference, Melbourne, VIC, Australia, October 25–27, 2023, Proceedings (pp. 245–259). Berlin: Springer-Verlag. doi:10.1007/978-981-99-7254-8_19
  87. Yang, X., Liu, Z., Tang, X., Lu, R., & Liu, B. (2024). An Efficient and Multi-Private Key Secure Aggregation Scheme for Federated Learning. IEEE Transactions on Services Computing, 17(5), 1998–2011. doi:10.1109/TSC.2024.3451165.
  88. Li, Y., Xu, G., Meng, X., Du, W., & Ren, X. (2024). LF3PFL: A Practical Privacy-Preserving Federated Learning Algorithm Based on Local Federalization Scheme. Entropy, 26(5), 353. doi:10.3390/e26050353.
  89. Hayashitani, M., Mori, J., & Teranishi, I. (2025). Survey of Privacy Threats and Countermeasures in Federated Learning. 2025 3rd International Conference on Federated Learning Technologies and Applications (FLTA), (pp. 78-85). doi:10.1109/FLTA67013.2025.11336767
  90. Abd El-Kareem Abd El-Moaty Saleh, H., Fernández Vilas, A., Fernández-Veiga, M., El-Sonbaty, Y., & El-Bendary, N. (2022). Using Decentralized Aggregation for Federated Learning with Differential Privacy. Proceedings of the 19th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &Amp; Ubiquitous Networks on 19th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, 33–39. doi:10.1145/3551663.3558682.
  91. Wang, L., Zhu, T., Zhou, W., & Yu, P. S. (2025). Linkage on security, privacy and fairness in federated learning: New balances and new perspectives. Neural Networks, 192, 107874. doi:10.1016/j.neunet.2025.107874.
  92. Hodorog, A., Petri, I., & Rezgui, Y. (2022). Machine learning and Natural Language Processing of social media data for event detection in smart cities. Sustainable Cities and Society, 85, 104026. doi:10.1016/j.scs.2022.104026.
  93. Yan, F., Zhang, M., Wei, B., Ren, K., & Jiang, W. (2024). SARD: Fake news detection based on CLIP contrastive learning and multimodal semantic alignment. Journal of King Saud University - Computer and Information Sciences, 36, 102160. doi:https://doi.org/10.1016/j.jksuci.2024.102160
  94. Derakhshan, M., & Mohammadi, F. (2025). Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes. 2025 3rd International Conference on Foundation and Large Language Models (FLLM), (pp. 939-945). doi:10.1109/FLLM67465.2025.11391132
  95. Yu, Z., Qu, Q., Chen, X., & Wang, C. (2025). Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. doi:10.1109/icassp49660.2025.10887714.
  96. Liu, S., Li, J., Zhao, G., Zhang, Y., Meng, X., Yu, F. R., Ji, X., & Li, M. (2025). EventGPT: Event Stream Understanding with Multimodal Large Language Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR, 29139–29149. doi:10.1109/cvpr52734.2025.02713.
  97. Liu, Y. L., Blodgett, S. L., Cheung, J. C. K., Liao, Q. V., Olteanu, A., & Xiao, Z. (2024). ECBD: Evidence-Centered Benchmark Design for NLP. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 16349–16365. doi:10.18653/v1/2024.acl-long.861.
Contributions of Science and Technology for Engineering
Volume 3, Issue 2
February 2026
Pages 11-28

Files

History

  • Receive Date: 31 August 2025
  • Revise Date: 23 October 2025
  • Accept Date: 19 November 2025
  • First Publish Date: 19 November 2025
  • Publish Date: 01 February 2026

Share

How to cite

Statistics