Social media has proven to be a safe space for people with mental illness to express themselves, a place where they are more willing to discuss their condition, treatment, and feelings. Thus, social media represents an important source of information for the analysis of the informal expression of the physical and mental responses to taking psychiatric drugs. In this paper, we propose a deep learning-based method to characterize drug reactions from a socialomics perspective. To this end, we construct seven base entity networks, one for each of five psychological entity types (affective, cognitive, perceptual, social, and personal concerns) and one each for side effects and disease. We then calculate the similarities between two entities (i.e., nodes) as the weight of the edges. Each node is represented by a combined vector consisting of semantic and graph embeddings. For each drug, we create a drug network and measure the variation in the network structure generated by adding the drug network to the seven base entity networks. If the variation in the network structure of a particular base network is larger than the others, it means that the drug has a larger impact on that base network. These results demonstrate that drug reactions can be assessed using social media, which may aid in the understanding of these reactions.

事实证明，社交媒体是精神疾病患者表达自我的安全空间，在这个地方，他们更愿意讨论自己的病情，治疗方法和感受。因此，社交媒体是用于分析服用精神科药物的身心反应的非正式表达的重要信息来源。在本文中，我们提出了一种基于深度学习的方法来从社会经济学角度描述药物反应。为此，我们构建了七个基本实体网络，一个针对五种心理实体类型（情感，认知，感知，社会和个人关注），另一种针对副作用和疾病。然后，我们将两个实体（即节点）之间的相似度计算为边缘的权重。每个节点由包含语义和图嵌入的组合向量表示。对于每种药物，我们创建一个药物网络并测量通过将药物网络添加到七个基本实体网络而生成的网络结构的变化。如果特定基础网络的网络结构变化比其他基础网络大，则意味着药物对该基础网络的影响更大。这些结果表明，可以使用社交媒体评估药物反应，这可能有助于理解这些反应。这意味着该药物对该基本网络的影响更大。这些结果表明，可以使用社交媒体评估药物反应，这可能有助于理解这些反应。这意味着该药物对该基本网络的影响更大。这些结果表明，可以使用社交媒体评估药物反应，这可能有助于理解这些反应。