School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.
Comput Methods Programs Biomed. 2023 Oct;240:107702. doi: 10.1016/j.cmpb.2023.107702. Epub 2023 Jul 6.
Depression can severely impact physical and mental health and may even harm society. Therefore, detecting the early symptoms of depression and treating them on time is critical. The widespread use of social media has led individuals with depressive tendencies to express their emotions on social platforms, share their painful experiences, and seek support and help. Therefore, the massive available amounts of social platform data provide the possibility of identifying depressive tendencies.
This paper proposes a neural network hybrid model MTDD to achieve this goal. Analysis of the content of users' posts on social platforms has facilitated constructing a post-level method to detect depressive tendencies in individuals. Compared with existing methods, the MTDD model uses the following innovative methods: First, this model is based on social platform data, which is objective and accurate, can be obtained at a low cost, and is easy to operate. The model can avoid the influence of subjective factors in the depressive tendency detection method based on consultation with mental health experts. In other words, it can avoid the problem of undisclosed and imperfect data in depressive tendency detection. Second, the MTDD model is based on a deep neural network hybrid model, combining the advantages of CNN and BiLSTM networks and avoiding the problem of poor generalization ability in a single model for depression tendency recognition. Third, the MTDD model is based on multimodal features for learning the vector representation of depression-prone text, including text features, semantic features, and domain knowledge, making the model more robust.
Extensive experimental results demonstrate that our MTDD model detects users who may have a depressive tendency with a 95% F1 value and obtained SOTA results.
Our MTDD model can detect depressive users on social media platforms more effectively, providing the possibility for early diagnosis and timely treatment of depression. The experiment proves that our MTDD model outperforms many of the latest depressive tendency detection models.
抑郁症会严重影响身心健康，甚至危害社会。因此，及时发现抑郁症的早期症状并进行治疗至关重要。社交媒体的广泛使用，使得有抑郁倾向的个体在社交平台上表达自己的情绪，分享自己的痛苦经历，并寻求支持和帮助。因此，大量的社交平台数据提供了识别抑郁倾向的可能性。
本文提出了一种神经网络混合模型 MTDD 来实现这一目标。对用户在社交平台上发布的内容进行分析，促进了构建一种基于帖子的方法来检测个体的抑郁倾向。与现有方法相比，MTDD 模型采用了以下创新方法：首先，该模型基于社交平台数据，客观准确，成本低，易于操作。该模型可以避免基于心理健康专家咨询的抑郁倾向检测方法中主观因素的影响。换句话说，它可以避免抑郁倾向检测中未公开和不完善数据的问题。其次，MTDD 模型基于深度神经网络混合模型，结合了 CNN 和 BiLSTM 网络的优势，避免了单一模型在抑郁倾向识别中泛化能力差的问题。第三，MTDD 模型基于多模态特征学习易患抑郁文本的向量表示，包括文本特征、语义特征和领域知识，使模型更加健壮。
大量实验结果表明，我们的 MTDD 模型可以以 95%的 F1 值检测出可能有抑郁倾向的用户，并且取得了 SOTA 结果。
我们的 MTDD 模型可以更有效地检测社交媒体平台上的抑郁用户，为抑郁症的早期诊断和及时治疗提供了可能。实验证明，我们的 MTDD 模型优于许多最新的抑郁倾向检测模型。
