We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media posts and combines these representations using an attentive bidirectional GRU network. We incorporate affective information, by augmenting the text representations with features extracted from a pretrained emotion classifier. Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. Our analysis indicates that morality and profanity can be important features for depression detection. We apply our model for depression detection on Reddit posts on the Pirina dataset, and further consider the setting of detecting depressed users, given multiple posts per user, proposed in the Reddit RSDD dataset. The inclusion of the proposed features yields state-of-the-art results in both settings, namely 2.65% and 6.73% absolute improvement in F1 score respectively. Index Terms: Depression detection, BERT, Feature fusion, Emotion recognition, profanity, morality

翻译：我们提出了一种用于从社交媒体帖子中检测抑郁症的深度架构。所提出的架构基于BERT从社交媒体帖子中提取语言表示，并使用注意力双向GRU网络将这些表示进行组合。通过利用从预训练情感分类器中提取的特征增强文本表示，我们融入了情感信息。受心理学文献启发，我们提出采用后期融合方案，将帖子及单词中的辱骂性和道德性特征整合到架构中。我们的分析表明，道德性和辱骂性可作为抑郁症检测的重要特征。我们将该模型应用于Pirina数据集上的Reddit帖子抑郁症检测，并进一步考虑了Reddit RSDD数据集中针对每个用户多个帖子的抑郁用户检测场景。所提出特征的引入在两种场景下均取得了最优结果，F1分数分别绝对提升2.65%和6.73%。索引术语：抑郁症检测、BERT、特征融合、情感识别、辱骂性、道德性