• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.227-253
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• 2023

The study is a preliminary study to conceptualize memes as information resources for literacy education in information environment changed with digital revolution. The study is to explain the context and reality of memes in order to promote the utilization of memes as information resources. The research questions are as follows: First, what topics are 'memes' studied with? Second, what things are captured and studied as 'memes'? The study conducted frequency and co-occurrence network analysis on 145 domestic studies and contents analysis on 73 domestic studies. The results are as follows: First, memes were mainly studied in the fields of 'humanities', 'social sciences', 'interdiciplinary studies', and 'arts and kinesiology'. Studies based on Dawkins' concept of memes (around 2012), studies on introducing the concept of memes to explain the spread of Korean Wave content (around 2015), and independent studies of memes as a major research topic in cultural sociology (around 2019) were performed. Second, memes are linguistic. Language memes (L-memes) are 102 (37%), language-visual memes (LV-memes) are 23 (8%), language-visual-musical memes (LVM-memes) are 21 (8%). Keyword 'language meme' ranked high in frequency, degree centrality and betweenness centrality of co-occurrence network. In other words, memes are expanding as a unique information phenomenon of cultural sociology based on linguistic characteristics. It is necessary to conceptualize meme literacy in terms of information literacy.

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.