{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T09:43:06Z","timestamp":1725615786044},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"\n Online social networks (OSNs) are a rich source of information, and the data (including user-generated content) can be mined to facilitate real-world event prediction. However, the dynamic nature of OSNs and the fast-pace nature of social events or hot topics compound the challenge of event prediction. This is a key limitation in many existing approaches. For example, our evaluations of six baseline approaches (i.e., logistic regression latent Dirichlet allocation (LDA)-based logistic regression (LR), multi-task learning (MTL), long short-term memory (LSTM) and convolutional neural networks, and transformer-based model) on three datasets collected as part of this research (two from Twitter and one from a news collection site\n \n 1<\/jats:sup>\n <\/jats:xref>\n ), reveal that the accuracy of these approaches is between 50% and 60%, and they are not capable of utilizing new events in event predictions. Hence, in this article, we develop a novel DNN-based framework (hereafter referred to as event prediction with feedback mechanism\u2014\n EPFM<\/jats:monospace>\n . Specifically, EPFM makes use of a feedback mechanism based on emerging events detection to improve the performance of event prediction. The feedback mechanism ensembles three outlier detection processes and returns a list of new events. Some of the events will then be chosen by analysts to feed into the fine-tuning process to update the predictive model. To evaluate EPFM, we conduct a series of experiments on the same three datasets, whose findings show that EPFM achieves 80% accuracy in event detection and outperforms the six baseline approaches.We also validate EPFM\u2019s capability of detecting new events by empirically analyzing the feedback mechanism under different thresholds.\n <\/jats:p>","DOI":"10.1145\/3522759","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T11:33:15Z","timestamp":1648639995000},"page":"1-24","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Social Media Event Prediction using DNN with Feedback Mechanism"],"prefix":"10.1145","volume":"13","author":[{"given":"Wanlun","family":"Ma","sequence":"first","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, VIC, Australia"}]},{"given":"Xiangyu","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Ultimo, NSW, Australia"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"James Cook University, Douglas, QLD, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0655-666X","authenticated-orcid":false,"given":"Sheng","family":"Wen","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, VIC, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9208-5336","authenticated-orcid":false,"given":"Kkwang Raymond","family":"Choo","sequence":"additional","affiliation":[{"name":"University of Texas at San Antonio, San Antonio, TX"}]},{"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, VIC, Australia"}]}],"member":"320","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3416703"},{"key":"e_1_3_3_3_2","first-page":"1763","article-title":"Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency","author":"Allington Daniel","year":"2020","unstructured":"Daniel Allington, Bobby Duffy, Simon Wessely, Nayana Dhavan, and James Rubin. 2020. 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