• Cross-Cultural Studies
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• v.35
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• pp.237-268
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• 2014

Examining swear words found in Korean and English, we aim to answer the following two questions: (i) 'What words develop into swear words?' and (ii) 'Why they do?' The utility of a swear word is frequently recognized as intimidation directed towards an opponent, emotional catharsis, and solidarity building among in-group members (Jay 1992, 2000, Kim 1997). We seek to go beyond this simple enumeration of possible functions of swearing and suggest an underlying mechanism at work to explain how these functions are achieved and why only certain types of words are employed in this pursuit. A close examination reveals that a swear word must contain either taboo or sadism as an essential component. Sexual pleasure adds another dimension to the basic components. Thus, if an expression contains a subset of the component set {taboo, sadism, sex} in its semantics, it becomes available for swearing (one of the underlined components must be included in the set). For example, many religiously sacred expressions and words for excretion are common swear words as they violate social and religious taboo. On the other hand, words referring to social minorities are a convenient target for sadism. Furthermore, words describing sexual activity contain all three components, violating social taboo, evoking sadism, and giving the initiator guilty sexual pleasure. A combination of the components can produce an emotional effect called catharsis for the initiator. When directed towards others, these components, especially taboo and sadism, can be exploited as a verbal attack, an intimidation, preceding or replacing a physical attack. However, solidarity building is analyzed as a secondary function of swearing, achieved by sharing a sense of accomplice when in-group members behave badly together, such as violating social taboo and committing sadism.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.25-34
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• 2022

With the development of online services, the ripple effect of negative comments is increasing, and the damage of cyber violence is rising. Various methods such as filtering based on forbidden words and reporting systems prevent this, but it is challenging to eradicate negative comments. Therefore, this study aimed to increase the accuracy of the classification of negative comments using deep learning and blur the parts corresponding to profanity. Two different conditional training helped decide the number of deep learning layers and filters. The accuracy of 88% confirmed with 90% of the dataset for training and 10% for tests. In addition, Grad-CAM enabled us to find and blur the location of swear words in negative comments. Although the accuracy of classifying comments based on simple forbidden words was 56%, it was found that blurring negative comments through the deep learning model was more effective.