• Korean journal of communication and information
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• v.80
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• pp.115-142
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• 2016

This study aims to reveal the content of and logic behind a recent negative public sentiment toward female idols with the example of a debate over songstress IU's fourth album that was released late last year. While previous studies on fandom have focused on the identification process towards entertainers and making community, a recent phenomenon of "anti-fandom" or "malicious comments" implies that more research is needed on negative emotions such as hostility or schadenfreude (feelings of pleasure from others' misfortunes). Schadenfreude is a social sentiment that originated in modern liberalism, which features contradictions between public equality and private ownership, and that has been intensified in neoliberalism, which features a maximization of this contradiction centering on a meritocracy. Celebrities in Korea often become the targets of schadenfreude, which is associated with the suspicion that they gain popularity not from their abilities but from "just being popular." It should also be noted that this kind of schadenfreude operates differently between male and female entertainers. Specifically, the acquisition of money and fame by modern women whose presence used to be located in the private possessions of males is considered to be due to their unjustified use of sexuality. This is also the background of the recent online misogyny culture in Korea. In this context, IU, who had been successful at building a differentiated image of "sister-like idol artist," became a valid target. Although accusing IU of utilizing pedophilia reflects a stalemate that a current politics of sexual violence faces, it rather damages the name of an individual than attracts public attention to the structural causes of childsexualabuse. This is why I see the way that pedophilia was used in the debate over IU as a schadenfreude. Consequently, the term pedophilia here contributes to an expansion of the entertainment economy that is sustained by rises and falls of the celebrities' stock prices.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.