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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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85206445377&origin=inward; http://dx.doi.org/10.1007/978-3-031-73068-9_5; https://link.springer.com/10.1007/978-3-031-73068-9_5; https://dx.doi.org/10.1007/978-3-031-73068-9_5; https://link.springer.com/chapter/10.1007/978-3-031-73068-9_5
Springer Science and Business Media LLC
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