描述和评估社交媒体上的心理健康错误信息:基于质量和深度学习的研究
Xiyuan Yang1, Kexin Ba2, Jiushu Xie1
1School of Psychology, Nanjing Normal University, Nanjing, China.
Cyberpsychology, behavior and social networking
|December 7, 2025
概括
这项研究开发了一个深度学习框架,以检测社交媒体上的心理健康错误信息. 像BERT和GRU这样的模型在评估内容可信度方面表现有希望,尽管在评估证据质量方面仍然存在挑战.
科学领域:
- 计算社会科学 计算社会科学
- 心理健康信息学心理健康信息学
- 人工智能的人工智能
背景情况:
- 社交媒体加速了错误信息的传播,在心理健康领域带来了风险.
- 由于数据量和复杂性,手动调节在线错误信息是不可行的.
- 准确评估心理健康信息可信度至关重要.
研究的目的:
- 提出一个综合框架,结合定性分析和深度学习,用于自动检测和评估心理健康错误信息.
- 开发和验证在线心理健康内容的精细可信度评估框架.
- 训练和评估深度学习模型,以评估与心理健康相关的社交媒体帖子的可信度.
主要方法:
- 根据专家采访和有根据的理论,制定了一个21级,七维的可信度评估框架.
- 手动注释了814个中国社交媒体帖子,以创建一个高质量的数据集.
- 在数据集上训练并评估了Gated Recurrent Unit (GRU),变压器双向编码器表示 (BERT) 和稳定优化的BERT方法 (RoBERTa) 模型.
主要成果:
- 伯特,格鲁和罗伯塔模型有效地评估了心理健康错误信息,使用情绪线索和表面模式,特别是炎症表达和表达的一面性.
- 所有模型都在评估证据质量和检测上下文依赖的错误信息方面遇到了困难.
- 在解决复杂的评估挑战时,BERT和GRU在逻辑严谨度等维度上表现优于RoBERTa.
结论:
- 拟议的框架提供了一种强大,可扩展和专家知情的方法,以提高在线心理健康信息的可信度.
- 深度学习模型显示了自动错误信息检测的潜力,但需要进一步细化证据质量等细微方面的细化.
- 未来的研究应该专注于提高模型在处理上下文依赖的错误信息和证据评估的能力.
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