Noise elimination techniques in social media big data : A survey
*Amitava SarderCorresponding authorsarder.amitava@gmail.comSchool of Computer ScienceSwami Vivekananda UniversityBarrackpore, West Bengal, 700121, India0000-0002-6210-5583View full profile → , Ranjan Kumar Mondalranjankm@svu.ac.inDepartment of Computer Science and EngineeringSwami Vivekananda UniversityBarrackpore, West Bengal, 700121, India0000-0002-4866-4004View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jul 2025
- Published Online:
- 15 Jul 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1585
- Pages:
- 743–785
Abstract
In the era of big data, social media platforms have become invaluable sources of information, offering vast amounts of data that can be analyzed to extract valuable insights. With the explosive growth of social media and the large volume of data it generates, noise has become a significant challenge in deriving meaningful insights. This article provides a comprehensive survey of noise elimination techniques in social media big data. In this paper, we review the literature on various noise elimination methods in social media big data and introduce a new classification of these strategies, such as preprocessing techniques, filtering methods, topic modelling, machine learning approaches, community detection, user-based filtering, outlier detection, anomaly detection, spam detection and related approaches. The survey examines the latest developments and recent advancements in noise reduction techniques, highlighting subcategories within each approach and their contributions to noise removal. Additionally, it discusses the challenges and opportunities associated with noise elimination in social media big data. This survey serves as a valuable resource for researchers and practitioners seeking an overview of noise reduction methods to improve the quality and reliability of social media big data analysis. Overall, this survey offers valuable insights into the landscape of noise elimination techniques in social media big data analysis, providing researchers and practitioners with a comprehensive understanding of existing methods and guiding future research in this important field.
Keywords
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References
[1] E. Olshannikova, T. Olsson, J. Huhtamäki, and H. Kärkkäinen, “Conceptualizing big social data,” J. Big Data, vol. 4, Art. no. 3 (Jan. 2017), doi: 10.1186/s40537-017-0063-x.
[2] H. Xiong, G. Pandey, M. Steinbach, and V. Kumar, “Enhancing data analysis with noise removal,” IEEE Trans. Knowl. Data Eng., vol. 18, no. 3, pp. 304–319 (Mar. 2006), doi: 10.1109/TKDE.2006.46.
[3] V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced Naïve Bayes model,” in Proc. Int. Conf. Intelligent Data Engineering and Automated Learning (IDEAL), Hefei, China, pp. 194–201 (2013), doi: 10.1007/978-3-642-41278-3_24.
[4] J. A. Sáez, M. Galar, J. Luengo, and F. Herrera, “INFFC: An iterative class noise filter based on the fusion of classifiers,” Inf. Fusion, vol. 27, pp. 19–32 (Jan. 2016), doi: 10.1016/j.inffus.2015.04.002.
[5] A. Waldherr, D. Maier, P. Miltner, and E. Günther, “Big data, big noise: The challenge of finding issue networks on the web,” Soc. Sci. Comput. Rev., vol. 35, no. 4, pp. 427–443 (Aug. 2017), doi: 10.1177/0894439316643050.
[6] Y. Kim, J. Huang, and S. Emery, “Garbage in, garbage out: Data collection, quality assessment and reporting standards for social media data,” J. Med. Internet Res., vol. 18, no. 2, Art. no. e41 (Feb. 2016), doi: 10.2196/jmir.4738.
[7] S. M. Arif and M. Mustapha, “The effect of noise elimination and stemming in sentiment analysis for Malay documents,” in Adv. Intell. Syst. Comput., Singapore, pp. 115–124 (2016), doi: 10.1007/978-981-10-2772-7_10.
[8] R. Kaur and R. Maini, “Performance evaluation and comparative analysis of different filters for noise reduction,” Int. J. Image Graph. Signal Process., vol. 8, no. 7, pp. 9–21 (Jul. 2016), doi: 10.5815/ijigsp.2016.07.02.
[9] K. Al Sharou, Z. Li, and L. Specia, “Towards a better understanding of noise in natural language processing,” in Proc. Recent Advances in Natural Language Processing (RANLP), Online, pp. 53–62 (Sep. 2021), doi: 10.26615/978-954-452-072-4_007.
[10] J. Chen, Y. Huang, and J. Benesty, “Filtering techniques for noise reduction and speech enhancement,” in Adaptive Signal Processing: Applications to Real-World Problems, Berlin, Germany: Springer, pp. 129–154 (2003), doi: 10.1007/978-3-662-11028-7_5.
[11] Cesar F. Caiafa , Zhe Sun ,Toshihisa Tanaka , Pere Marti-Puig and Jordi Solé-Casals, “Machine learning methods with noisy, incomplete or small datasets,” Appl. Sci., vol. 11, no. 9, Art. no. 4132 (May 2021), doi: 10.3390/app11094132.
[12] S. Gupta and A. Gupta, “Dealing with noise problem in machine learning datasets: A systematic review,” Procedia Comput. Sci., vol. 161, pp. 466–474 (Nov. 2019), doi: 10.1016/j.procs.2019.11.146.
[13] Khalid M. Al-Gethami, Mousa T. Al-Akhras, and Mohammed Alawairdhi, “Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets,” Secur. Commun. Netw., vol. 2021, Art. no. 8836057 (2021), doi: 10.1155/2021/8836057.
[14] Li Wang, Jiang Wang, Yuanjun Bi, Weili Wu, Wen Xu, and Biao Lian, “Noise-tolerance community detection and evolution in dynamic social networks,” J. Comb. Optim., vol. 28, no. 3, pp. 600–612 (May 2014), doi: 10.1007/s10878-014-9719-z.
[15] O. Oleghe, “A predictive noise correction methodology for manufacturing process datasets,” J. Big Data, vol. 7, Art. no. 67 (2020), doi: 10.1186/s40537-020-00367-w.
[16] Daniela Mahl, Gerret von Nordheim, and Lars Guenther, “Noise Pollution: A Multi-Step Approach to Assessing the Consequences of (Not) Validating Search Terms on Automated Content Analyses,” Digital Journalism, vol. 11, no. 5, pp. 1–23 (2023), doi: 10.1080/21670811.2022.2114920.
[17] Diego García-Gil, Julián Luengo, Salvador García, and Francisco Herrera, “Enabling smart data: Noise filtering in big data classification,” Inf. Sci., vol. 479, pp. 135–152 (2019), doi: 10.1016/j.ins.2018.12.002.
[18] JV. Durga Prasad Jasti, Guttikonda Kranthi Kumar, M. Sandeep Kumar, V. Maheshwari, Prabhu Jayagopal, Bhaskar Pant, Alagar Karthick, and M. Muhibbullah, “Relevant-Based Feature Ranking (RBFR) Method for Text Classification Based on Machine Learning Algorithm,” J. Nanomater., vol. 2022, Art. no. 9238968 (2022), doi: 10.1155/2022/9238968.
[19] Nayana Pampapura Madali, Manar Alsaid, and Suliman Hawamdeh, “The impact of social noise on social media and the original intended message: BLM as a case study,” J. Inf. Sci., vol. 50, no. 1, pp. 1–17 (2024), doi: 10.1177/01655515221077347.
[20] Yiwei Feng, M. Asif Naeem, Farhaan Mirza, and Ali Tahir, “Reply using past replies — a deep learning-based e-mail client,” Electronics, vol. 9, no. 9, Art. no. 1353 (2020), doi: 10.3390/electronics9091353.
[21] Belal Abdullah Hezam Murshed, Suresha Mallappa, Jemal Abawajy, Mufeed Ahmed Naji Saif, Hasib Daowd Esmail Al-Ariki, and Hudhaifa Mohammed Abdulwahab, “Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis,” Artif. Intell. Rev., vol. 56, pp. 5133–5260 (2023), doi: 10.1007/s10462-022-10254-w.
[22] Joel Brynielsson, Fredrik Johansson, Carl Jonsson, and Anders Westling, “Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises,” Security Informatics, vol. 3, Art. no. 7 (2014), doi: 10.1186/s13388-014-0007-3.
[23] M. F. Uddin, “Noise removal and structured data detection,” in Proc. ASONAM, pp. 1156–1163 (2016), doi: 10.1109/ASONAM.2016.7752413.
[24] Fatma M. Talaat, Eman M. El-Gendy, Mahmoud M. Saafan, and Samah A. Gamel, “Utilizing social media and machine learning for personality and emotion recognition using PERS,” Neural Comput. Appl., vol. 35, no. 33, pp. 23927–23941 (2023), doi: 10.1007/s00521-023-08962-7.
[25] Marco Brambilla, Alireza Javadian Sabet, Kalyani Kharmale, and Amin Endah Sulistiawati, “Graph-based conversation analysis in social media,” Big Data Cogn. Comput., vol. 6, no. 4, Art. no. 113 (2022), doi: 10.3390/bdcc6040113.
[26] L. Akoglu, H. Tong, and D. Koutra, “Graph-based anomaly detection and description: A survey,” Data Min. Knowl. Discov., vol. 29, no. 3, pp. 626–688 (2015), doi: 10.1007/s10618-014-0365-y.
[27] Jiyoon Lee, Younghoon Kim, and Seoung Bum Kim, “Noise-robust graph-based semi-supervised learning with dynamic shaving label propagation,” Appl. Soft Comput., vol. 142, Art. no. 110371, ISSN 1568-4946 (2023), doi: 10.1016/j.asoc.2023.110371.
[28] R. Jain and W. Xu, “AI-based wrapper for high dimensional feature selection,” BMC Bioinformatics, vol. 24, Art. no. 392 (2023), doi: 10.1186/s12859-023-05502-x.
[29] Simone Pau, Alessandra Perniciano, Barbara Pes, and Dario Rubattu, “An Evaluation of Feature Selection Robustness on Class Noisy Data,” Information, vol. 14, no. 8, Art. no. 438 (2023), doi: 10.3390/info14080438.
[30] J. M. Johnson and T. M. Khoshgoftaar, “Classifying big data with label noise: A survey,” ACM J. Data Inf. Qual., vol. 14, no. 4, Art. no. 23 (2022), doi: 10.1145/3492546.
[31] Nadhia Salsabila Azzahra, Danang Triantoro Murdiansyah, and Kemas M. Lhaksmana, “Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection,” Int. J. Inf. Commun. Technol., vol. 7, no. 1, pp. 64–76 (2021), doi: 10.21108/ijoict.v7i1.552.
[32] T. Zimmerman, “Social noise and information behavior,” J. Doc., vol. 78, no. 2, pp. 345–362 (2022), doi: 10.1108/JD-08-2021-0165.
[33] Zahra Nematzadeh, Roliana Ibrahim, and Ali Selamat, “A hybrid model for class noise detection using kmeans and classification filtering algorithms,” SN Appl. Sci., vol. 2, Art. no. 1207 (2020), doi: 10.1007/s42452-020-3129-x.
[34] Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Ebrahim Mahdipour, and Seyed Mahdi Jameii, “A systematic review of techniques, open issues, and future directions,” Telemat. Inform., vol. 57, Art. no. 101517 (2021), doi: 10.1016/j.tele.2020.101517.
[35] M. Li and C. Zhu, “Noisy label processing for classification: A survey,” arXiv preprint arXiv:2404.04159 (2024). [Online]. Available: https://arxiv.org/abs/2404.04159
[36] Kazi Toufique Elahi, Tasnuva Binte Rahman, Shakil Shahriar, Samir Sarker, Md. Tanvir Rouf Shawon, and G. M. Shahariar, “A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts,” arXiv preprint arXiv: 2401.14360 (2024). [Online]. Available: https://arxiv.org/abs/2401.14360
[37] A. Saseendran, L. Setia, V. Chhabria, D. Chakraborty and A. Barman Roy, “Impact of Noise in Dataset on Machine Learning Algorithms,” preprint (2019). [Online]. Available: https://doi.org/10.13140/RG.2.2.25669.91369
[38] L. P. F. Garcia, A. C. Lorena and A. C. P. L. F. Carvalho, “A Study on Class Noise Detection and Elimination,” 2012 Brazilian Symposium on Neural Networks, Curitiba, Brazil, pp. 13-18 (2012), doi: 10.1109/SBRN.2012.49.
[39] P. S. Rao, R. N. Sushma, and Anurag, “Study and Analysis of Noise Effect on Big Data Analytics,” Int. J. Manag., Technol. Eng., vol. 8, no. 12, pp. 5841–5850 (2018).
[40] R. Alabduljabbar, H. Almazrou, and A. Aldawod, “Context-Aware News Recommendation System: Incorporating Contextual Information and Collaborative Filtering Techniques,” Int. J. Comput. Intell. Syst., vol. 16, no. 1 (2023). [Online]. Available: https://doi.org/10.1007/s44196-023-00315-5
[41] R. Kaur and R. Maini, “Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction,” Int. J. Image Graph. Signal Process., vol. 8, no. 7, pp. 9–21 (2016). Available :10.5815/ijigsp.2016.07.02
[42] M. Goutay, F. A. Aoudia and J. Hoydis, “Deep Reinforcement Learning Autoencoder with Noisy Feedback,” 2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), Avignon, France, pp. 1-6 (2019), doi: 10.23919/WiOPT47501.2019.9144089.
[43] Rajmadhan Ekambaram, Sergiy Fefilatyev, Matthew Shreve, Kurt Kramer, Lawrence O. Hall, Dmitry B. Goldgof, and Rangachar Kasturi, ‘Active cleaning of label noise’, Pattern Recognition, vol. 51, pp. 463–480 (2016). Available: https://doi.org/10.1016/j.patcog.2015.09.020
