Ujaran Kebencian terhadap Perempuan di Media Sosial: Kajian Semantik

Abstract

Gender-based hate speech can happen to anyone, both women and men. However, because we live in an environment with a very strong patriarchal culture, the vulnerability to hate speech is more likely to occur in women. The perpetrators can be anyone, be it family, close friends, or other members of the general public who are not even known. In 2020, according to the National Commission on Violence Against Women, there were around 1,300 cases of hate speech experienced by women.(Agustian, 2020). One form of violence on social media is writing harsh sentences as an expression of cursing, slandering, harassing, laughing at or mocking, even degrading the dignity of women carried out by perpetrators who have social media accounts. Hate speech is closely related to speech verbs. Speech verbs are words that describe processes, actions, or conditions. This study discusses hate speech verbs against women on social media with a study of Natural Semantic Metalanguage by Wierzbicka (2007). The purpose of this study is to find the right semantic parameters to identify hate speech verbs against women on social media, semantically categorize hate speech verbs, explain the meaning of hate speech verbs, find the causes of hate speech often occurring to 3 women in the comments column of Youtube and Twitter and design a digital literacy flow diagram to prevent hate speech on social media. This study uses a qualitative approach as a research procedure producing descriptive data in the form of written words. The data in this study are the writings contained in the comments column on X and Youtube which show hate speech against three women, namely Sri Mulyani, Nazwa Shihab and Agnez Mo. The method used in data collection is the reading method. The reading method is to carefully read written data sources related to the assumption of hate speech. Through this study, the researcher concludedfound a parameter in determining hate speech on social media. Without the use of parameters, it is difficult to avoid the entry of other phenomena into the realm of speech verbs on social media. Next is the breakdown of the complexity related to the principle of categorizing hate speech verbs on social media. The categorization of hate speech verbs found four categorizations of hate speech verbs against 3 women on social media. (1) X said something bad to Y because X wanted Y to feel something bad. This category is the category with the most types of UK verbs. These verbs are insulting, insinuating, cursing, insulting, and mocking. (2) X said something bad about Y to Z so that Z would think something bad about Y. Regarding this category, there is only one type of UK verb, namely inciting. (3) X said something bad to Y so that something bad would happen to Y. This categorization also only contains one hate speech verb, namely threatening. (4) X said something bad to Y because X wanted to say something bad about Y. In this categorization, there are two types of UK verbs, namely accusing and slandering. Next, hate speech verbs still have different semantic characteristics from each other. This difference can be observed based on a comparison between two or more verbs with close meanings. Then it was found that the cause of hate speech often occurs towards women on social media because of several communication components that were analyzed using the Hymes approach. The last finding was that a digital literacy flow design with a linguistic approach could be carried out by providing digital literacy education through the MSA, LSF and Pragmatic approaches.