{"title":"使用K-core, M-core和Km-core方法分析Facebook用户粘性图表","authors":"Thidawan Klaysri","doi":"10.1145/3456172.3456215","DOIUrl":null,"url":null,"abstract":"Customer engagement in Facebook fan page of a brand can be rationalized as a network from customer reactions towards the moderator postings. In this paper the network of consumers connected by the posts of two supermarket chains are represented in different forms of graphs. Here a graph analytic framework, which adopts the concept of Social Network Analysis to examine the structure of the graphs, is presented. The graph filtering methods, k-core, m-core or m-slice and km-core, a combination of the former cores, are utilized to examine the customer engagement behavior, to identify and to filter the consumer communities. For both supermarket brands, most of the customer attitudes toward the advertising and promotion posts are positive. Their customer engagement behaviors are similar, in that the majority of customers are engaged by a single post advertising a discount promotion, greater than 90%, following power-laws with respect to the threshold of consumer degree and co-reaction posts. There are around 3% of customers consuming both brands.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Facebook Customer Engagement Graph Analysis Using K-core, M-core and Km-core Methods\",\"authors\":\"Thidawan Klaysri\",\"doi\":\"10.1145/3456172.3456215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Customer engagement in Facebook fan page of a brand can be rationalized as a network from customer reactions towards the moderator postings. In this paper the network of consumers connected by the posts of two supermarket chains are represented in different forms of graphs. Here a graph analytic framework, which adopts the concept of Social Network Analysis to examine the structure of the graphs, is presented. The graph filtering methods, k-core, m-core or m-slice and km-core, a combination of the former cores, are utilized to examine the customer engagement behavior, to identify and to filter the consumer communities. For both supermarket brands, most of the customer attitudes toward the advertising and promotion posts are positive. Their customer engagement behaviors are similar, in that the majority of customers are engaged by a single post advertising a discount promotion, greater than 90%, following power-laws with respect to the threshold of consumer degree and co-reaction posts. There are around 3% of customers consuming both brands.\",\"PeriodicalId\":133908,\"journal\":{\"name\":\"Proceedings of the 2021 7th International Conference on Computing and Data Engineering\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 7th International Conference on Computing and Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3456172.3456215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3456172.3456215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Facebook Customer Engagement Graph Analysis Using K-core, M-core and Km-core Methods
Customer engagement in Facebook fan page of a brand can be rationalized as a network from customer reactions towards the moderator postings. In this paper the network of consumers connected by the posts of two supermarket chains are represented in different forms of graphs. Here a graph analytic framework, which adopts the concept of Social Network Analysis to examine the structure of the graphs, is presented. The graph filtering methods, k-core, m-core or m-slice and km-core, a combination of the former cores, are utilized to examine the customer engagement behavior, to identify and to filter the consumer communities. For both supermarket brands, most of the customer attitudes toward the advertising and promotion posts are positive. Their customer engagement behaviors are similar, in that the majority of customers are engaged by a single post advertising a discount promotion, greater than 90%, following power-laws with respect to the threshold of consumer degree and co-reaction posts. There are around 3% of customers consuming both brands.