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Speak Up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

机译:说起来,反击!检测性骚扰的社交媒体披露

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摘要

The #MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak up and narrate personal experiences of such harassment. The aggregation and analysis of such disclosures pave the way to the development of technology-based prevention of sexual harassment. We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment. We propose the Disclosure Language Model, a three-part ULMFiT architecture, consisting of a Language model, a Medium-Specific (Twitter) model, and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this, to show that using a Discourse Language Model often yields better classification performance over (i) Generic deep learning based sentence classification models (ii) existing models that rely on handcrafted stylistic features. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology.
机译:#METOO运动是社交媒体上持续的普遍存存现象,旨在通过提供平台来说,展示性骚扰的频率和普遍普遍,以说明和叙述这种骚扰的个人经历。这些披露的聚集与分析为基于技术的防止性骚扰的发展铺平了道路。我们争辩说,通用句子分类模型中缺乏特异性可能不是解决文本微妙之处的最佳方法,该文本的分类任务在本质上以识别性骚扰的披露为主。我们提出了披露语言模型,由语言模型,中等特定(Twitter)模型以及任务特定的分类器组成的三部分ULMFIT架构,以解决此问题并创建手动注释的真实数据集进行测试我们的技术,表明使用话语语言模型通常会产生更好的分类性能超过(i)依赖手工传感器特征的现有模型的通用深度学习的句子分类模型(ii)。与最先进的通用和特定模型的广泛比较以及详细的错误分析为我们提出的方法提供了这种情况。

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