Identification of Toxicity in MultimediaMessages for Controlling Cyberbullying on Social Media by Natural Language Processing

V. Nithyashree, B. Hiremath, L. Vanishree, Aparna Duvvuri, Disha Anand Madival, G. Vidyashree
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Abstract

Speaking hatefully is an antisocial conduct. Hate can be expressed on the basis of gender, race, or religion, ethnic, etc. The definition of “hate speech” is ambiguous. The Hate speech is defined by the Council of the European Union as “any types of expression which disseminate, provoke, or defend intolerance as a reason for racial hatred, xenophobia, antisemitism, or other types of hatred, including violent nationalism and ethnocentrism, as well as prejudice and hatred toward migrants, minorities, and those with immigrant backgrounds. The most pressing issue in recent times in social media and online groups is toxicity identification sites for networking. Therefore, it is necessary to create an automatic hazardous identification system to keep people out of and restrict their access to these online settings. The prevalence of hate speech presents significant difficulties for the cyber culture. Users can wish for social media sites and online forums to support anti-hate discourse. Hate speech detection, however, is still a young technology, and system designers must come up with a way to identify unwanted hate speech while upholding the atmosphere of online freedom of speech. No method for detecting excellence has yet been put forth. Identifying the form of communication is hate speech and automatically recognising the hate speech are the two typical obstacles for a hate speech detection task. People must first decide which kinds of speech fall under the category of hate speech before screening it out. The majority of social media platforms define hate speech differently. In this day and age, Internet is necessary, and ethics has to be followed, however several parties spread hate speech deviating on race, ethnicity, and religion. The user’s freedom and anonymity increase the harassment by hate speech. It also adds lack of regulation on social media communication. Cyber bullying and trolling are two kinds of abusive behaviour.
自然语言处理对控制社交媒体网络欺凌的多媒体信息毒性的识别
说话充满仇恨是一种反社会的行为。仇恨可以基于性别、种族、宗教、民族等来表达。“仇恨言论”的定义是模糊的。欧洲联盟理事会将仇恨言论定义为“传播、煽动或捍卫不容忍的任何形式的表达,以此作为种族仇恨、仇外心理、反犹太主义或其他类型仇恨的理由,包括暴力民族主义和种族中心主义,以及对移民、少数民族和移民背景者的偏见和仇恨。”最近在社交媒体和网络群体中最紧迫的问题是毒性识别网站。因此,有必要创建一个自动危险识别系统,以防止人们进入并限制他们访问这些在线设置。仇恨言论的盛行给网络文化带来了巨大的困难。用户可以希望社交媒体网站和在线论坛支持反仇恨言论。然而,仇恨言论检测仍然是一项年轻的技术,系统设计师必须想出一种方法来识别不受欢迎的仇恨言论,同时维护在线言论自由的氛围。目前还没有提出一种方法来检测优秀。识别仇恨言论的传播形式和自动识别仇恨言论是仇恨言论检测任务的两个典型障碍。人们必须首先决定哪些言论属于仇恨言论的范畴,然后再将其筛选出来。大多数社交媒体平台对仇恨言论的定义不同。在这个时代,网络是必要的,道德是必须遵守的,但是一些政党散布了种族、民族和宗教的仇恨言论。用户的自由和匿名性增加了仇恨言论的骚扰。它还增加了对社交媒体传播缺乏监管的问题。网络欺凌和网络挑衅是两种虐待行为。
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