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Dual Attention based Suicide Risk Detection on Social Media

机译:基于双重关注的社交媒体自杀风险检测

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摘要

Despite some progress have been made in social media based suicide risk detection. However, visual information form post is often ignored when doing suicide risk detection. In this study, we propose a dual attention mechanism to capture the implicit correlation between text and image from the same post and utilize the visual information to improve the performance on social media based suicide risk detection. Experimental results on 5,000 Sina Weibo users show that with dual attention mechanism, our suicide risk detection model (DAM) can obtain 90% high accuracy.
机译:尽管基于社交媒体的自杀风险检测取得了一些进展。但是,在进行自杀风险检测时,通常会忽略视觉形式的信息发布。在这项研究中,我们提出了一种双重关注机制来捕获同一帖子中文本和图像之间的隐式关联,并利用视觉信息来提高基于社交媒体的自杀风险检测的性能。在5,000名新浪微博用户上的实验结果表明,通过双重关注机制,我们的自杀风险检测模型(DAM)可以获得90%的高精度。

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