Misogynous MEME Recognition: A Preliminary Study

Fersini, Elisabetta; Rizzi, Giulia; Saibene, Aurora; Gasparini, Francesca

doi:10.1007/978-3-031-08421-8_19

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Automatic hate speech detection in online platforms has been previously studied across different hate speech domains such as misogyny (Fersini et al, 2022;Plaza-Del-Arco et al, 2020), xenopho-bia (Romero-Vega et al, 2020Benitez-Andrades et al, 2022), homophobia (Karayigit et al, 2022;Arcila-Calderón et al, 2021) and others (Davidson et al, 2017;Lozano et al, 2017). Nevertheless, a limited number of works focus on Spanish data to develop ML-based systems for online hateful content identification.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Zero-Shot transfer Scenarios across Spanish variants for Hate Speech Detection

Castillo-lópez,

Riabi,

Seddah

2023

Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

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Hate speech detection in online platforms has been widely studied in the past. Most of these works were conducted in English and a few rich-resource languages. Recent approaches tailored for low-resource languages have explored the interests of zero-shot cross-lingual transfer learning models in resource-scarce scenarios. However, languages variations between geolects such as American English and British English, Latin-American Spanish, and European Spanish is still a problem for NLP models that often relies on (latent) lexical information for their classification tasks. More importantly, the cultural aspect, crucial for hate speech detection, is often overlooked.In this work, we present the results of a thorough analysis of hate speech detection models performance on different variants of Spanish, including a new hate speech toward immigrants Twitter data set we built to cover these variants. Using mBERT and Beto, a monolingual Spanish Bert-based language model, as the basis of our transfer learning architecture, our results indicate that hate speech detection models for a given Spanish variant are affected when different variations of such language are not considered. Hate speech expressions could vary from region to region where the same language is spoken. * Work conducting during an internship at Inria Paris. 1 Please be aware that this paper contains some examples of offensive slurs that may be considered upsetting.

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Section: Related Workmentioning

confidence: 99%

Analyzing Zero-Shot transfer Scenarios across Spanish variants for Hate Speech Detection

Castillo-lópez,

Riabi,

Seddah

2023

Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

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“…Ouaari et al [16] use neural networks to extract features of internet memes and train a classifier to identify the sentiment expressed in memes. Fersini et al [17] posit that hateful content is expressed through memes and, to support with their detection, they utilize unimodal and multimodal approaches to identify misogynous memes.…”

Section: Related Work On Meme Emotion Analysismentioning

confidence: 99%

NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

Guo¹,

Ma²,

Zubiaga³

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.

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