社会化媒体
自杀意念
构思
计算机科学
身份(音乐)
判决
互联网隐私
面子(社会学概念)
计算机安全
深度学习
人工智能
编码器
编码
身份盗窃
自杀行为
数据科学
自杀预防
心理学
机器学习
万维网
毒物控制
社会学
医学
医疗急救
认知科学
社会科学
生物化学
物理
化学
声学
基因
操作系统
作者
K Nikhileswar,D Vishal,L Sphoorthi,Fathimabi Shaik
出处
期刊:2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)
日期:2021-10-07
卷期号:: 1741-1747
被引量:7
标识
DOI:10.1109/icosec51865.2021.9591887
摘要
Numerous initiatives have been developed to prevent suicide. Still, people are not seeking help considering it will dishonor them in society, and they are unwilling to reveal their identity. However, thanks to social media platforms, people are more willing to express themselves anonymously as they do not directly face anyone. Our work aims to use this vast data to identify texts containing suicidal thoughts and help early detection of suicide using machine learning and deep learning techniques. The dataset used is collected by taking posts from "SuicideWatch" and "teenagers" Subreddits of Reddit using, "Pushshift" API. The proposed model uses a Universal Sentence Encoder to encode the text and a Fully connected Neural Network(FCNN) to classify text into suicide, and non-suicide, which achieved a better accuracy compared to the existing methods.
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