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A Review of an Automated Model for Sexist Language Detection and Replacement of Sexist Terms

Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 2203 CCIS, Page: 45-58
2025
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Conference Paper Description

The perception of anonymity provided by the digital environment has contributed to the rise in harassment and hate speech in recent years on social media platforms. Particularly sexism and gender-based harassment have risen to frightening heights, increasing the number of people worldwide who experience or witness online abuse. Such offensive content frequently contains extremely vulgar and disparaging language, which causes great injury and distress to its targets. In this research paper we propose a way to automatically identify and replace insults directed at people based on their gender on social networks in order to solve this urgent problem and ultimately create a more secure and civil online community. Various machine learning and deep learning models are used to detect sexually explicit information, enabling thorough investigation using a range of performance measures to pinpoint the most efficient models. This research also includes the application of a word replacement algorithm to guarantee that abusive words completely lose their destructive context. The research seeks to reduce the negative effects of abusive information and promote a more positive digital environment by routinely substituting harsh language with neutral or constructive substitutes.

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