{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:01:40Z","timestamp":1777705300130,"version":"3.51.4"},"reference-count":7,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>Spotting rumors from social media and intervening early has always been a daunting challenge. In recent years, Deep neural networks have begun to discover rumors by exploring the way of rumor propagation. The existing static graph models either only focus on the spatial structure information of rumor propagation or on time series propagation information but do not effectively combine them. This paper proposes the Static Spatiotemporal Model (SSM), which first extracts the textual semantic information and constructs undirected and directed propagation trees. Then obtains spatial structure information of rumor propagation through Graph Convolutional Network and extracts time series propagation information through the Recurrent Neural Network. The extracted spatiotemporal information is enhanced using different source node information hopping. Finally, SSM uses a weighted connection ensemble to rumor classification. Experimentally validated on datasets such as Weibo and Twitter, the results show that the proposed method outperforms several state-of-the-art static graph models. To better apply SSM in early detection and characterize early concepts, this paper presents a new data collection index for early detection, which can detect events that spread faster and have more significant influence in a targeted manner. The experimental results on the new indicators further verify the superiority of SSM as it can extract sufficient information in early detection or events with fewer participants.<\/jats:p>","DOI":"10.3233\/jifs-220417","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T11:34:34Z","timestamp":1668166474000},"page":"2847-2862","source":"Crossref","is-referenced-by-count":3,"title":["Rumor detection model fused with static spatiotemporal information"],"prefix":"10.1177","volume":"44","author":[{"given":"Biao","family":"Wang","sequence":"first","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongquan","family":"Wei","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxin","family":"Liu","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-220417_ref18","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1609\/aaai.v34i01.5393","article-title":"Rumor detection on social media with bi-directional graph convolutional networks","volume":"34","author":"Bian","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"7","key":"10.3233\/JIFS-220417_ref19","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.1007\/s10489-020-02036-0","article-title":"Detection ofrumor conversations in Twitter using graph convolutional networks","volume":"51","author":"Lotfi","year":"2021","journal-title":"Applied Intelligence"},{"issue":"2","key":"10.3233\/JIFS-220417_ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3377478","article-title":"Fake news early detection: A theory-driven model","volume":"1","author":"Zhou","year":"2020","journal-title":"Digital Threats: Research and Practice"},{"issue":"1","key":"10.3233\/JIFS-220417_ref22","doi-asserted-by":"crossref","first-page":"e0168344","DOI":"10.1371\/journal.pone.0168344","article-title":"Rumor detection over varying timewindows","volume":"12","author":"Kwon","year":"2017","journal-title":"PloS One"},{"issue":"6","key":"10.3233\/JIFS-220417_ref27","doi-asserted-by":"crossref","first-page":"102712","DOI":"10.1016\/j.ipm.2021.102712","article-title":"Temporally evolving graph neural networkfor fake news detection","volume":"58","author":"Song","year":"2021","journal-title":"Information Processing & Management"},{"issue":"6380","key":"10.3233\/JIFS-220417_ref30","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false newsonline","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"10.3233\/JIFS-220417_ref35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/6659430","article-title":"An End-to-End Rumor Detection Model Based on FeatureAggregation","volume":"2021","author":"Ye","year":"2021","journal-title":"Complexity"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-220417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:27Z","timestamp":1777455807000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-220417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":7,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-220417","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}