{"title":"政府社交媒体内容特征及其对公民参与率的影响","authors":"Diah Henisa, Nori Wilantika","doi":"10.1109/ICIMCIS53775.2021.9699299","DOIUrl":null,"url":null,"abstract":"Social media has been used by the government for communication, socialization, and publication. However, it is still not being used to its maximum potential. This study aims to analyze citizen engagement on a government agency's social media accounts based on the number of likes, comments, and shares. In addition, this study examines how citizen engagement differs depending on the topic, media type, and upload time of the post on government social media accounts. Data was collected from Facebook, Twitter, and Instagram using scraping tools, namely Facepager, Twint, and Instaloader. The types of post topics are determined based on the results of topic modeling using the Latent Dirichlet Allocation (LDA) method. Using the Kruskal-Wallis test and Dunn's test, the post topic, the media type, and the post time has a significant influence on citizen engagement. Instagram got the highest engagement rate compared to Facebook and Twitter.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Content Characteristics of Government Social Media and The Impact on Citizen Engagement Rate\",\"authors\":\"Diah Henisa, Nori Wilantika\",\"doi\":\"10.1109/ICIMCIS53775.2021.9699299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media has been used by the government for communication, socialization, and publication. However, it is still not being used to its maximum potential. This study aims to analyze citizen engagement on a government agency's social media accounts based on the number of likes, comments, and shares. In addition, this study examines how citizen engagement differs depending on the topic, media type, and upload time of the post on government social media accounts. Data was collected from Facebook, Twitter, and Instagram using scraping tools, namely Facepager, Twint, and Instaloader. The types of post topics are determined based on the results of topic modeling using the Latent Dirichlet Allocation (LDA) method. Using the Kruskal-Wallis test and Dunn's test, the post topic, the media type, and the post time has a significant influence on citizen engagement. Instagram got the highest engagement rate compared to Facebook and Twitter.\",\"PeriodicalId\":250460,\"journal\":{\"name\":\"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIMCIS53775.2021.9699299\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIMCIS53775.2021.9699299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Content Characteristics of Government Social Media and The Impact on Citizen Engagement Rate
Social media has been used by the government for communication, socialization, and publication. However, it is still not being used to its maximum potential. This study aims to analyze citizen engagement on a government agency's social media accounts based on the number of likes, comments, and shares. In addition, this study examines how citizen engagement differs depending on the topic, media type, and upload time of the post on government social media accounts. Data was collected from Facebook, Twitter, and Instagram using scraping tools, namely Facepager, Twint, and Instaloader. The types of post topics are determined based on the results of topic modeling using the Latent Dirichlet Allocation (LDA) method. Using the Kruskal-Wallis test and Dunn's test, the post topic, the media type, and the post time has a significant influence on citizen engagement. Instagram got the highest engagement rate compared to Facebook and Twitter.