{"title":"Do Directionality and Network Size Affect Network Structure in Online Social Networks?","authors":"N. Mayande, C. Weber","doi":"10.23919/PICMET.2019.8893732","DOIUrl":null,"url":null,"abstract":"A study of the online social networks of six Twitter conversations about six YouTube product categories reveals that directionality and network size affect the structure of online social networks. Our results indicate that large networks tend to be non-random, regardless of whether they are directional or not, suggesting that structural attributes of the online networks under study are a true reflection of network's features. Smaller non-directional networks also tend to be non-random, whereas smaller directional networks tend to be random in nature. However, very small networks tend to be random in nature, whether they are directional or not. Our results suggest that larger online networks undergo different generation mechanisms than smaller real-world networks, especially if these networks are directional. Extant theory, which is almost exclusively derived from observation of real-world networks, may thus not adequately describe the behavior of online networks. We propose research to remedy this deficiency at the end of this paper.","PeriodicalId":390110,"journal":{"name":"2019 Portland International Conference on Management of Engineering and Technology (PICMET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Portland International Conference on Management of Engineering and Technology (PICMET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/PICMET.2019.8893732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A study of the online social networks of six Twitter conversations about six YouTube product categories reveals that directionality and network size affect the structure of online social networks. Our results indicate that large networks tend to be non-random, regardless of whether they are directional or not, suggesting that structural attributes of the online networks under study are a true reflection of network's features. Smaller non-directional networks also tend to be non-random, whereas smaller directional networks tend to be random in nature. However, very small networks tend to be random in nature, whether they are directional or not. Our results suggest that larger online networks undergo different generation mechanisms than smaller real-world networks, especially if these networks are directional. Extant theory, which is almost exclusively derived from observation of real-world networks, may thus not adequately describe the behavior of online networks. We propose research to remedy this deficiency at the end of this paper.