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在中国社交媒体中挖矿自杀思想:双通道深度学习模型与信息获取优化
Xiuyang Meng1,2, Xiaohui Cui1,2, Yue Zhang1,2
1School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了DSI-BTCNN,这是一种用于识别中国社交媒体上的自杀念头的深度学习模型. 它增强了早期发现,并支持了预防自杀的努力.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 公共卫生 公共卫生
背景情况:
- 在社交媒体上发现自杀念头对于预防自杀至关重要.
- 非结构化的社交媒体数据对准确的识别提出了挑战.
- 现有的模型可能无法完全捕捉中文文本中的语言细微差别.
研究的目的:
- 开发一种新的基于中国的双通道深度学习模型 (DSI-BTCNN),用于识别自杀念头.
- 增强模型处理文本本地,上下文和逻辑结构的能力.
- 在实时社交媒体监控中提高自杀念头检测的效率和稳定性.
主要方法:
- 一个双通道深度学习架构,具有多个卷积内核.
- 一个细粒度的文本增强方法,用于中国数据.
- 一个基于信息获取的IDFN融合机制,利用值评估进行特征分配.
主要成果:
- DSI-BTCNN模型实现了高性能:89.64%的准确性,92.84%的精度,89.24%的F1得分,96.50%的AUC.
- 在关键指标上表现明显优于TextCNN和BiLSTM模型.
- 值增加了17.53%,这表明检测能力得到了增强.
结论:
- DSI-BTCNN模型为检测中国社交媒体数据中的自杀念头提供了强大而有效的解决方案.
- 细粒度文本增强和IDFN融合机制有助于改进功能挖掘和计算效率.
- 该模型为实时监测和早期干预自杀预防计划提供了有前途的工具.
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