Najla Althuniyan, J. Sirrianni, Md Mahfuzer Rahman, X. Liu
{"title":"大型网络辩论移动App的设计与分析","authors":"Najla Althuniyan, J. Sirrianni, Md Mahfuzer Rahman, X. Liu","doi":"10.1109/TransAI49837.2020.00013","DOIUrl":null,"url":null,"abstract":"People from different backgrounds share opinions about various issues over the Internet. The resulted discussions contain substantial information, from which we can derive the collective intelligence and the crowd wisdom. Several argumentation platforms have been developed to enable online deliberations with large-scale in-depth argumentation for effective online discussions. These platforms host structured argumentation networks that allow complex analytical models to mine the argumentation for collective intelligence. However, not all of those argumentation platforms were developed mobile applications. In this paper, we contribute with the design of a mobile application for cyber-argumentation. This mobile application supports intelligent cyber-argumentation and large-scale discussions and provides meaningful analytics on mobile devices. The platform has incorporated several analytical models to capture collective opinions, detect opinion polarization, and predict missing user opinions. An example is used to illustrate our design and models, and a system usability study of our application is presented. This application is an initial step to bring the multi-sided argumentation and deliberation into handheld devices and shows the potential in bringing multi-sided large-scale cyber-argumentation into the limited screen sizes platforms.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"36 22","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and Analysis of Mobile App for Large-Scale Cyber-Argumentation\",\"authors\":\"Najla Althuniyan, J. Sirrianni, Md Mahfuzer Rahman, X. Liu\",\"doi\":\"10.1109/TransAI49837.2020.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"People from different backgrounds share opinions about various issues over the Internet. The resulted discussions contain substantial information, from which we can derive the collective intelligence and the crowd wisdom. Several argumentation platforms have been developed to enable online deliberations with large-scale in-depth argumentation for effective online discussions. These platforms host structured argumentation networks that allow complex analytical models to mine the argumentation for collective intelligence. However, not all of those argumentation platforms were developed mobile applications. In this paper, we contribute with the design of a mobile application for cyber-argumentation. This mobile application supports intelligent cyber-argumentation and large-scale discussions and provides meaningful analytics on mobile devices. The platform has incorporated several analytical models to capture collective opinions, detect opinion polarization, and predict missing user opinions. An example is used to illustrate our design and models, and a system usability study of our application is presented. This application is an initial step to bring the multi-sided argumentation and deliberation into handheld devices and shows the potential in bringing multi-sided large-scale cyber-argumentation into the limited screen sizes platforms.\",\"PeriodicalId\":151527,\"journal\":{\"name\":\"2020 Second International Conference on Transdisciplinary AI (TransAI)\",\"volume\":\"36 22\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Second International Conference on Transdisciplinary AI (TransAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TransAI49837.2020.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Second International Conference on Transdisciplinary AI (TransAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TransAI49837.2020.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design and Analysis of Mobile App for Large-Scale Cyber-Argumentation
People from different backgrounds share opinions about various issues over the Internet. The resulted discussions contain substantial information, from which we can derive the collective intelligence and the crowd wisdom. Several argumentation platforms have been developed to enable online deliberations with large-scale in-depth argumentation for effective online discussions. These platforms host structured argumentation networks that allow complex analytical models to mine the argumentation for collective intelligence. However, not all of those argumentation platforms were developed mobile applications. In this paper, we contribute with the design of a mobile application for cyber-argumentation. This mobile application supports intelligent cyber-argumentation and large-scale discussions and provides meaningful analytics on mobile devices. The platform has incorporated several analytical models to capture collective opinions, detect opinion polarization, and predict missing user opinions. An example is used to illustrate our design and models, and a system usability study of our application is presented. This application is an initial step to bring the multi-sided argumentation and deliberation into handheld devices and shows the potential in bringing multi-sided large-scale cyber-argumentation into the limited screen sizes platforms.