{"title":"让我们团结起来,#清除庇护所:影响在线社交网络中用户网络中心的因素","authors":"Ezgi Akar","doi":"10.4018/jitr.299943","DOIUrl":null,"url":null,"abstract":"This study explores the factors contributing to online users’ network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. We performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users’ betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. We applied a path analysis including users’ centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user’s in-degree connections in the network. Together with the user’s out-degree connections along with in-degree links pushed a user to have a strategic place in the network. We also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Let's Get United and #ClearTheShelters: The Factors Contributing to Users' Network Centrality in Online Social Networks\",\"authors\":\"Ezgi Akar\",\"doi\":\"10.4018/jitr.299943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study explores the factors contributing to online users’ network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. We performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users’ betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. We applied a path analysis including users’ centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user’s in-degree connections in the network. Together with the user’s out-degree connections along with in-degree links pushed a user to have a strategic place in the network. We also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.\",\"PeriodicalId\":296080,\"journal\":{\"name\":\"J. Inf. Technol. Res.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Inf. Technol. Res.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/jitr.299943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Inf. Technol. Res.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/jitr.299943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Let's Get United and #ClearTheShelters: The Factors Contributing to Users' Network Centrality in Online Social Networks
This study explores the factors contributing to online users’ network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. We performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users’ betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. We applied a path analysis including users’ centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user’s in-degree connections in the network. Together with the user’s out-degree connections along with in-degree links pushed a user to have a strategic place in the network. We also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.