{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:05:08Z","timestamp":1772910308801,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61966017"],"award-info":[{"award-number":["61966017"]}],"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":["61662032"],"award-info":[{"award-number":["61662032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Humanities and Social Sciences Project of Jiangxi Province","award":["JC19121"],"award-info":[{"award-number":["JC19121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This study aims to explore the time series context and sentiment polarity features of rumors\u2019 life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors\u2019 classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.<\/jats:p>","DOI":"10.3390\/fi13110267","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T21:40:21Z","timestamp":1635198021000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Rumor Detection Based on Attention CNN and Time Series of Context Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Yun","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0493-9990","authenticated-orcid":false,"given":"Jianmei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15860","DOI":"10.1109\/ACCESS.2021.3052566","article-title":"An Analysis of the sentiment Evolution of Large-Scale Internet Public Opinion Events Based on the BERT-LDA Hybrid Model","volume":"9","author":"Tan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"114","article-title":"Automatic Rumor Identification on Microblog","volume":"57","author":"He","year":"2021","journal-title":"Libr. 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