Some experiments on the use of Natural Language Processing for sexism detection and classification in social media

4 pages•Published: February 16, 2023

Roi Santos-Rios, Jesús Vilares and Miguel A. Alonso

Abstract

As the world’s digital population grows, so does the reach and usage of social media: in 2021, 56% of the global population were social media users [1]. Social networks are now a part of our everyday life and continue to transform the way we interact with others on a global scale The downside is that negative behaviors in social interactions are also increasing their presence. For example, between March 1 and April 30, the OBERAXE (Spanish Observatory of Racism and Xenophobia) has detected a 27% increase in hate speech on social networks with respect to the previous two-month period [2]. In this paper we target the detection and classification of sexist content in social media texts. Two tasks are considered: (i) a binary classification task to decide whether a given text is sexist or not; and (ii) a multiclass classification task according to the type of sexism present in it.

Keyphrases: natural language processing, sentiment analysis, sexist content, text classification

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 24-27.

Links:	https://easychair.org/publications/paper/rdrm
	https://doi.org/10.29007/8z6l

BibTeX entry

@inproceedings{XoveTIC2022:Some_experiments_use_Natural,
  author    = {Roi Santos-Rios and Jesús Vilares and Miguel A. Alonso},
  title     = {Some experiments on the use of Natural Language Processing for sexism detection and classification in social media},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/rdrm},
  doi       = {10.29007/8z6l},
  pages     = {24-27},
  year      = {2023}}

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