{"title":"How to adopt mass customization strategy: Understanding the role of consumers' perceived brand value","authors":"Zhenhao Li, Hong-Bing Yang, Jing Xu","doi":"10.2139/ssrn.4079770","DOIUrl":"https://doi.org/10.2139/ssrn.4079770","url":null,"abstract":"","PeriodicalId":10663,"journal":{"name":"Comput. Ind. Eng.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89848152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social media plays a prominent role in the spread of mass shootings. It brought about a significant contagious effect on future similar incidents. Therefore, we explore Machine Learning (ML) models to forecast the change in the public’s attitudes about mass shootings on social media over time. These ML models include Support Vector Machine (SVM), Logistic Regression (LR), and the optimized Deep Neural Networks based on an Improved Particle Swarm Optimization algorithm (IPSO-DNN). We then propose a self-excited contagion model to predict the number of mass shootings by focusing on the spread of public attitudes on Twitter. Moreover, we also improve the proposed contagion model with the consideration of social distancing and the daily growth rate of COVID-19 cases, to predict and analyze mass shootings under the COVID-19 pandemic. Experimental results demonstrate that the proposed contagion models perform very well in predicting future mass shootings in the United States.
{"title":"Exploring the contagion effect of social media on mass shootings","authors":"Dixizi Liu, Z. Dong, Guo Qiu","doi":"10.2139/ssrn.4078393","DOIUrl":"https://doi.org/10.2139/ssrn.4078393","url":null,"abstract":"Social media plays a prominent role in the spread of mass shootings. It brought about a significant contagious effect on future similar incidents. Therefore, we explore Machine Learning (ML) models to forecast the change in the public’s attitudes about mass shootings on social media over time. These ML models include Support Vector Machine (SVM), Logistic Regression (LR), and the optimized Deep Neural Networks based on an Improved Particle Swarm Optimization algorithm (IPSO-DNN). We then propose a self-excited contagion model to predict the number of mass shootings by focusing on the spread of public attitudes on Twitter. Moreover, we also improve the proposed contagion model with the consideration of social distancing and the daily growth rate of COVID-19 cases, to predict and analyze mass shootings under the COVID-19 pandemic. Experimental results demonstrate that the proposed contagion models perform very well in predicting future mass shootings in the United States.","PeriodicalId":10663,"journal":{"name":"Comput. Ind. Eng.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89942758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joe Hoecherl, M. Robbins, B. Borghetti, R. R. Hill
{"title":"Partially autoregressive machine learning: Development and testing of methods to predict United States Air Force retention","authors":"Joe Hoecherl, M. Robbins, B. Borghetti, R. R. Hill","doi":"10.2139/ssrn.4069621","DOIUrl":"https://doi.org/10.2139/ssrn.4069621","url":null,"abstract":"","PeriodicalId":10663,"journal":{"name":"Comput. Ind. Eng.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77249954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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