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It is essential to analyze and forecast user behavior on these platforms to improve user experience, implement targeted advertising, and reduce unpleasant interactions. This study aimed to create deep learning-based algorithms for social media user behavior analysis and prediction. The first data corpus we analyzed comprises tweets on the general elections in India in 2019. The data prepossessing process used Z-score normalization and missing value-relevant features to normalize the data\u2019s scale. Features were extracted from high-dimensional data using the linear regression (LR) model, which preserves important information representations for the prediction model to improve computing efficiency and reduce overfitting. The term frequency-inverse document frequency (TF-IDF) numerical statistic was used to compute this. We suggested optimizing the socially restricted Boltzmann machine dove particle swarm with bi-directional long short-term memory (SRBM-DPSO-bi-LSTM) to analyze and predict social media user behavior. Training data optimize the specified deep learning algorithms for essential performance measures and frequently enhance the prediction system to keep up with social media user behavior. This will make it possible to evaluate how well the suggested solution works regarding prediction rate, accuracy, recall, F1-score, and the AUC-ROC curve. As a result, this proposed deep learning approach to prediction data to improve Social Media User Behavior. <\/jats:p>","DOI":"10.1142\/s0218126625501543","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T10:43:34Z","timestamp":1734432214000},"source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Framework for Social Media User Behavior Analysis and Prediction"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5895-6886","authenticated-orcid":false,"given":"Jing","family":"Dang","sequence":"first","affiliation":[{"name":"Faculty of Communication Arts, Communication Arts, Bangkokthonburi University, Thawi Watthana, 10170 Bangkok, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7921-1737","authenticated-orcid":false,"given":"Mo","family":"Bi","sequence":"additional","affiliation":[{"name":"Southeast University, School of Foreign Languages, Nanjing 211189, China"},{"name":"Southeast University, Regional and Country Studies Institute, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8814-1292","authenticated-orcid":false,"given":"Jingze","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Intelligent Finance and Business, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1241-6293","authenticated-orcid":false,"given":"Yichen","family":"Sun","sequence":"additional","affiliation":[{"name":"High School Affiliated to The University of Nottingham Ningbo China, Ningbo 315000, P.\u00a0R.\u00a0China"},{"name":"Department of Architecture and Built Environment, University of Nottingham, Ningbo 315154, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"key":"S0218126625501543BIB001","first-page":"47","volume":"53","author":"Shahbaznezhad H.","year":"2021","journal-title":"J. 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