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<p>Modern developments in social networks and internet technologies have drawn researchers from around the world to investigate cyber bullying meme detection systems for detecting targeted bullying text and images in females, known as misogynistic text and images. Based on the reviewed literature, very few studies have employed BiLSTM and VVCG16 models on the multimedia automatic misogynistic identification (MAMI) dataset via bidirectional long-short-term memory (BiLSTM) and visual geometry group 16 (VGG16). Hence, this study aims to develop a misogynistic text and image detection system using bidirectional long short-term memory (BiLSTM) for text classification and visual geometry group 16 (VGG16) for image classification. The methodology integrates the visual geometry group 16 (VGG16) and bidirectional long-short-term memory (BiLSTM) models for multimedia analysis. The MAMI dataset, which includes 12,000 misogynistic memes split into training and test sets in the ratio of 75% to 25%, is utilized. Furthermore, this study addresses two primary subtasks. Subtask A involves classifying memes either as misogynistic or nonmisogynistic, and subtask B aims at identifying the type of misogyny, which includes stereotypes, objectification, shaming, and violence. The analysis and visualization of these categories were conducted via pandas, NumPy, spacy, unicodedata and scikit learning. The performance of the system was enhanced through the application of both the CNN and RNN algorithms on the MAMI dataset, which yielded the best performance, with an accuracy of 0.99 on both text and image data.</p>

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Automatic Misogyny Detection using Supervised Artificial Neural Networks

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Olanike Christianah Akinduyite,
  • Ogheneruno Chris-Umoru

摘要

Modern developments in social networks and internet technologies have drawn researchers from around the world to investigate cyber bullying meme detection systems for detecting targeted bullying text and images in females, known as misogynistic text and images. Based on the reviewed literature, very few studies have employed BiLSTM and VVCG16 models on the multimedia automatic misogynistic identification (MAMI) dataset via bidirectional long-short-term memory (BiLSTM) and visual geometry group 16 (VGG16). Hence, this study aims to develop a misogynistic text and image detection system using bidirectional long short-term memory (BiLSTM) for text classification and visual geometry group 16 (VGG16) for image classification. The methodology integrates the visual geometry group 16 (VGG16) and bidirectional long-short-term memory (BiLSTM) models for multimedia analysis. The MAMI dataset, which includes 12,000 misogynistic memes split into training and test sets in the ratio of 75% to 25%, is utilized. Furthermore, this study addresses two primary subtasks. Subtask A involves classifying memes either as misogynistic or nonmisogynistic, and subtask B aims at identifying the type of misogyny, which includes stereotypes, objectification, shaming, and violence. The analysis and visualization of these categories were conducted via pandas, NumPy, spacy, unicodedata and scikit learning. The performance of the system was enhanced through the application of both the CNN and RNN algorithms on the MAMI dataset, which yielded the best performance, with an accuracy of 0.99 on both text and image data.