Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces

• URL: http://arxiv.org/abs/2404.02013v2
• Date: Wed, 3 Apr 2024 09:14:01 GMT
• Title: Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces
• Authors: Advaitha Vetagiri, Gyandeep Kalita, Eisha Halder, Chetna Taparia, Partha Pakray, Riyanka Manna,
• Abstract summary: Team CNLP-NITS-PP developed an ensemble approach combining CNN and BiLSTM networks. CNN captures localized features indicative of abusive language through its convolution filters applied on embedded input text. BiLSTM analyzes this sequence for dependencies among words and phrases. validation scores showed strong performance across f1-measures, especially for English 0.84.
• Score: 0.6543929004971272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online gender-based harassment is a widespread issue limiting the free expression and participation of women and marginalized genders in digital spaces. Detecting such abusive content can enable platforms to curb this menace. We participated in the Gendered Abuse Detection in Indic Languages shared task at ICON2023 that provided datasets of annotated Twitter posts in English, Hindi and Tamil for building classifiers to identify gendered abuse. Our team CNLP-NITS-PP developed an ensemble approach combining CNN and BiLSTM networks that can effectively model semantic and sequential patterns in textual data. The CNN captures localized features indicative of abusive language through its convolution filters applied on embedded input text. To determine context-based offensiveness, the BiLSTM analyzes this sequence for dependencies among words and phrases. Multiple variations were trained using FastText and GloVe word embeddings for each language dataset comprising over 7,600 crowdsourced annotations across labels for explicit abuse, targeted minority attacks and general offences. The validation scores showed strong performance across f1-measures, especially for English 0.84. Our experiments reveal how customizing embeddings and model hyperparameters can improve detection capability. The proposed architecture ranked 1st in the competition, proving its ability to handle real-world noisy text with code-switching. This technique has a promising scope as platforms aim to combat cyber harassment facing Indic language internet users. Our Code is at https://github.com/advaithavetagiri/CNLP-NITS-PP

Related papers

• CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving [61.73180469072787]
  We focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture COSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules. COSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.
  arXiv  Detail & Related papers  (2024-06-16T16:10:51Z)
• Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias [8.168722337906148]
  This study investigates the presence of bias in harmful speech classification of gender-queer dialect online. We introduce a novel dataset, QueerLex, based on 109 curated templates exemplifying non-derogatory uses of LGBTQ+ slurs. We systematically evaluate the performance of five off-the-shelf language models in assessing the harm of these texts.
  arXiv  Detail & Related papers  (2024-05-23T18:07:28Z)
• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts [2.406214748890827]
  This paper proposes an approach to detection of online sexual predatory chats and abusive language using the open-source pretrained Llama 2 7B- parameter model. We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages (i.e., English, Roman Urdu and Urdu) Experimental results show a strong performance of the proposed approach, which performs proficiently and consistently across three distinct datasets.
  arXiv  Detail & Related papers  (2023-08-28T16:18:50Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• COLD: A Benchmark for Chinese Offensive Language Detection [54.60909500459201]
  We use COLDataset, a Chinese offensive language dataset with 37k annotated sentences. We also propose textscCOLDetector to study output offensiveness of popular Chinese language models. Our resources and analyses are intended to help detoxify the Chinese online communities and evaluate the safety performance of generative language models.
  arXiv  Detail & Related papers  (2022-01-16T11:47:23Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media [1.8899300124593648]
  Prevalence of Hindi-English code-mixed data (Hinglish) is on the rise with most of the urban population all over the world. Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages. We propose a methodology for efficient detection of unstructured code-mix Hinglish language.
  arXiv  Detail & Related papers  (2021-05-11T10:02:28Z)
• Evaluation of Deep Learning Models for Hostility Detection in Hindi Text [2.572404739180802]
  We present approaches for hostile text detection in the Hindi language. The proposed approaches are evaluated on the Constraint@AAAI 2021 Hindi hostility detection dataset. We evaluate a host of deep learning approaches based on CNN, LSTM, and BERT for this multi-label classification problem.
  arXiv  Detail & Related papers  (2021-01-11T19:10:57Z)
• Offensive Language Detection: A Comparative Analysis [2.5739449801033842]
  We explore the effectiveness of Google sentence encoder, Fasttext, Dynamic mode decomposition (DMD) based features and Random kitchen sink (RKS) method for offensive language detection. From the experiments and evaluation we observed that RKS with fastetxt achieved competing results.
  arXiv  Detail & Related papers  (2020-01-09T17:48:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.