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Child Abuse and Domestic Abuse: Content and Feature Analysis from Social Media Disclosures

机译:虐待儿童和家庭虐待:社交媒体披露的内容和特征分析

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Due to increase in popularity of social media, people have started discussing their thoughts and opinions in the form of textual posts. Currently, the people tend to disclose even the socially tabooed topics such as Child Abuse (CA), and Domestic Abuse (DA) to receive the desired response and social support in turn. The increasing volume of abuse related posts being shared on social media is of great interest for public health sectors and family welfare organizations to monitor the public health and promote support services. However, due to the large volume, high velocity and huge variety of context and content of user generated data, it is difficult to mine the different kinds of abuse (CA and DA) related posts from other general posts, that flood over the web. Hence, this paper aims to discover and differentiate the characteristics of CA and DA posts from the massive user generated posts, with the underlying context. Various features such as psycholinguistic, textual and sentimental features are analyzed and Machine Learning techniques are trained to analyze the predictive power of extracted features. Hence, the resulting model achieves more predictive power with high accuracy in classifying possible cases of abuse related posts from diverse user posts.
机译:由于社交媒体的普及,人们已经开始以文字帖子的形式讨论他们的想法和见解。当前,人们甚至倾向于披露社会上受禁忌的话题,例如虐待儿童(CA)和家庭虐待(DA),以便依次获得所需的回应和社会支持。在社交媒体上分享与虐待相关的帖子越来越多,这对公共卫生部门和家庭福利组织监测公共卫生和促进支持服务非常感兴趣。但是,由于用户生成的数据量大,速度快,上下文和内容的种类繁多,因此很难从网络上泛滥的其他普通帖子中挖掘与滥用相关的各种帖子(CA和DA)。因此,本文旨在通过潜在的上下文从大量用户生成的帖子中发现并区分CA和DA帖子的特征。分析了各种功能,例如心理语言,文本和情感特征,并且训练了机器学习技术以分析提取的特征的预测能力。因此,在对来自不同用户帖子的与滥用相关的帖子的可能情况进行分类的过程中,所得模型能够以更高的准确性获得更高的预测能力。

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