More than Just a Diagnosis: A Multi-Task Approach to Analyzing Bipolar Disorder on Reddit via DeMHeM
More than Just a Diagnosis: A Multi-Task Approach to Analyzing Bipolar Disorder on Reddit via DeMHeM
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DOI：
10.1109/bigdata59044.2023.10386934
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Rocco Zhang;Shailik Sarkar;Abdulaziz Alhamadani;Chang-Tien Lu
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文献类型：
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作者：
Rocco Zhang;Shailik Sarkar;Abdulaziz Alhamadani;Chang-Tien Lu
今天，精神健康问题影响着数百万人。虽然现有的从社交媒体文本预测心理健康状况的工作主要集中在抑郁症和类似的疾病上，但其他不太突出的疾病，如双相情感障碍，往往得不到深入的分析。此外，这些工作倾向于不分析或模拟这些不同疾病和条件的相关性质。为了解释多种心理健康状况的共存和相关性，本文引入了DeMHeM，这是一个新的多任务框架，旨在对Reddit等在线平台上的双相情感和相关心理健康话题进行描述性分类。通过将每个心理健康类别视为单独的任务，DeMHeM通过整合句子级和主题级嵌入来利用共享的潜在语义特征空间和特定于任务的语义特征空间。它进一步结合了联合学习的焦点损失、任务间参数共享和正则化衰减来优化对自然倾斜的不平衡数据集的预测。因此，该模型区分了不同的心理健康类别，并通过将每个帖子归入潜在的多个心理健康类别来模拟它们之间的相关性。接下来，我们将重点放在更有洞察力的分析上，利用模型的预测结果来研究讨论如何基于不同精神障碍的类型和共存而有所不同。我们通过应用我们训练的模型来预测一个类别，然后对每个预测的心理健康状况组合实施关键词提取技术，以了解双相情感障碍讨论中的具体细微差别，从而分析整个r/双相情感障碍子数据库。我们的结果表明，DeMHeM超过了基线模型，可以用来理解特定社区对心理健康主题的多方面讨论。
Mental health conditions affect millions of people today. While existing work on predicting mental health conditions from social media text focuses largely on depression and similar conditions, other less prominent disorders like bipolar tend not to receive in-depth analysis. Furthermore, these works tend not to analyze or model the correlated nature of these different disorders and conditions. To account for the coexistence and correlation of multiple mental health conditions, this paper introduces DeMHeM, a novel multitask framework designed for the descriptive classification of bipolar and related mental health topics on online platforms like Reddit. By treating each mental health category as a separate task, DeMHeM leverages both the shared latent and task-specific semantic feature space by integrating sentence-level and topic-level embeddings. It further incorporates Focal Loss for joint learning, inter-task parameter sharing, and regularization decay to optimize the prediction for the naturally skewed imbalanced dataset. Hence, the model distinguishes between different mental health categories and also models the correlation among them by categorizing each post into potentially multiple mental health categories. Next, we focus on a more insightful analysis by leveraging the predicted outcome of the model to study how the discussions differ based on the type and coexistence of different mental disorders. We analyze the entirety of the “r/bipolar” subreddit by applying our trained model to predict a category and then implementing keyword extraction techniques on each predicted combination of mental health conditions to understand the specific nuances in the discussion of bipolar disorder. Our results show that DeMHeM surpassed the baseline models and can be used to understand the multi-faceted discussion of mental health topics for a given community.