{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:07:03Z","timestamp":1784196423893,"version":"3.55.0"},"reference-count":35,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:00:00Z","timestamp":1782000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:00:00Z","timestamp":1782000000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971221"],"award-info":[{"award-number":["71971221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62477009"],"award-info":[{"award-number":["62477009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2025JJ50419"],"award-info":[{"award-number":["2025JJ50419"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Rapidly growing social media has become a key platform in recent years for influencing public sentiment and shaping hot public opinion. Against this backdrop, accurately predicting social users' opinions holds significant importance for public opinion analysis and guidance. Existing research generally focuses on the macro level of hot events. However, it fails to fully exploit the interaction data between high\u2010impact users and ordinary users. Moreover, such studies often neglect individual user characteristics and behavioural differences, which limits their capability in accurately forecasting user opinion trends. This paper proposes a novel approach to predict social users' opinions by leveraging high\u2010impact users' interaction data. The approach utilises the posts and interactive comments of high\u2010impact users to construct multiple user\u2013post opinion matrices and employs block\u2010wise matrix factorisation to extract the latent embeddings of users and posts. Building upon this, the historical comment\u2013post pairs are encoded using BERT, and a bidirectional cross\u2010attention mechanism is applied to model the semantic correlations between comments and posts, yielding cross\u2010attentional pair representations. To integrate these representations, a dynamic attention mechanism is used to weight and aggregate historical behaviours, generating an attention\u2010weighted semantic representation that is highly relevant to the current task. Finally, the multi\u2010source features of users and posts are fused through a transformer architecture and fed into a multi\u2010layer perceptron for opinion prediction. Experiments on a real\u2010world dataset demonstrate that the proposed approach achieves superior performance in user opinion prediction, showing promising potential for practical applications.<\/jats:p>","DOI":"10.1111\/exsy.70339","type":"journal-article","created":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T23:50:53Z","timestamp":1782085853000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging High\u2010Impact User Interactions for Social User Opinion Prediction: An Approach via Block\u2010Wise Matrix Factorisation and Attention Mechanism"],"prefix":"10.1111","volume":"43","author":[{"given":"Hua","family":"Ma","sequence":"first","affiliation":[{"name":"The College of Information Science and Engineering Hunan Normal University  Changsha China"},{"name":"The Hunan Provincial Key Laboratory of Philosophy and Social Sciences of Artificial Intelligence and International Communication  Changsha 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