Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
Source abstract
Abstract
Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hate Speech and Cyberbullying Detection · Terrorism, Counterterrorism, and Political Violence · Cybercrime and Law Enforcement Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
atlanTTic Research Center, Telecommunication Engineering School, Universidade de Vigo, 36310 Vigo, Spain
DATASALUS S. Coop Galega, 36201 Vigo, Spain
College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT)-Smart Village, P.O. Box 12676, Giza 3650111, Egypt
Funding
Cátedra Feminismos 4.0 Depo-Uvigo · Spanish Government · Xunta de Galicia (Centro de investigación de Galicia accreditation 2019-2022) · European Union (European Regional Development Fund - ERDF)
References (26)
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