• Asia pacific journal of information systems
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• v.25 no.2
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• pp.356-369
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• 2015

Public sectors around the world utilize social media tools and technologies in their daily activities for a variety of purposes, including disseminating useful information, fostering mass collaboration, and enforcing laws and regulations. A number of social media-based government stage models have emerged to document this use. In this chapter, we conducted a qualitative meta-synthesis of four social media-based government models. These models include 1) the open government maturity model, 2) the social media utilization model, 3) the adoption process for social media, and 4) the social media-based engagement model. The concepts, metaphors, and themes contained in these developmental models are extracted through a series of in-depth semantic analyses of descriptions, resulting in a common frame of reference.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.79-104
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• 2019

Recently, various methods of collecting and preserving social media have been discussed in the field of archives and records management as social media is actively used as a way of communicating with the public in the government. In the United States, there is a move to acquire and preserve social media at the government level, and the National Archives (UK) already provides social media archives to users through Twitter and YouTube. In this study, we proposed the features, acquisition methods of social media by type and the preservation model based on case study results in terms of acquisition and preservation of social media archiving. In order to verify the effectiveness of the proposed methods, this study was applied to the social media of Gyeongsangnamdo provincial government. The results of this study is meaningful in that it suggested acquisition and preservation methods through actual collected results and it is expected that it will be useful for establishing the models for future social media archiving.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.