Hierarchical Attention Network for Explainable Depression Detection on T witter Aided by Metaphor Concept Mappings (2022)

Abstract: Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals. Most existing black-box-like deep learning methods for depression detection largely focused on improving classification performance. However, explaining model decisions is imperative in health research because decision-making can often be high-stakes and life-and-death. Reliable automatic diagnosis of mental health problems including depression should be supported by credible explanations justifying models’ predictions. In this work, we propose a novel explainable model for depression detection on Twitter. It comprises a novel encoder combining hierarchical attention mechanisms and feed-forward neural networks. To support psycholinguistic studies, our model leverages metaphorical concept mappings as input. Thus, it not only detects depressed individuals, but also identifies features of such users’ tweets and associated metaphor concept mappings.

## introduction depression is a serious health and social issue that afflicts many individuals in modern society and its prevalence is predicted to increase globally. people with depression are likely to express their feelings and mental states over their social media before seeing health professionals @xcite . an automatic, efficient approach for depression identification is imperative to recommend adequate treatment, achieving remission and preventing relapse. recent studies on automatic depression detection on social media @xcite @xcite have largely focused on achieving higher detection accuracy. however, it is impossible to explain and interpret those black-box models that rely on state-of-the-art (sota) deep learning techniques. the recent development of explainable ai emphasizes that it is crucial for health professionals to fully comprehend, monitor and trust the ai decision-making mechanisms. people suffering from depression often use metaphors to describe their emotions and the experience of living with mental illness @xcite . in psychotherapy, metaphors are a pivotal tool for helping people with depression better understand themselves and their problems and facilitating effective communication between therapists and patients @xcite . this is because metaphorical expressions implicitly reflect people's different ways of understanding the same target. metaphor is not only a linguistic phenomenon, but also a reflection of cognitive mappings of source and target concepts @xcite . analyzing metaphor concept mappings (mcms) helps us understand the inner world of people with depression. metaphoric expressions associated with depression have been widely studied in psychology, particularly as a form of case studies @xcite . to the best of our knowledge, however, there has not been an automatic method that leverages mcm features extracted from a large corpus for depression detection. we are motivated to bridge the gap and offer better insights into automatic depression detection on social media and conceptual metaphor understanding. furthermore, we argue that psychological and psycholinguistic research communities can benefit from automated, explainable tools for studying the relationship between depression and metaphors. in this work, we propose an explainable framework foot_0 for depression detection on twitter, called hierarchical attention network @xcite . we propose a novel attention-based encoder which allows han to learn important inputs for user-level binary classification (i.e., depressed and non-depressed users). to further improve the interpretability of depression detection, we introduce mcms as an additional feature into the model. health professionals and potential patients can use learned features (i.e., characteristics of depressive tweets and mcms) as justification. we evaluate our model on a publicly available twitter depression detection dataset @xcite and show that han achieves the sota performance. it outperforms the strongest baseline @xcite by increasing an f1 score by 6.0% on average. additionally, our newly proposed encoder outperforms several classical encoders. in particular, han improves lstm @xcite (the most competitive benchmark encoder for our task) by 1.9% on a validation set with a quarter of the number of parameters of lstm. finally, we visualize and analyze examples of attention weights learned by han to demonstrate its explainability. the main contributions of this work can be summarized as follows: (1) we propose an explainable model for depression detection on twitter. unlike most sota methods employing attention mechanisms at word level @xcite , our model employs context-level attention mechanisms to identify the relative importance of certain tweets and metaphors, which is crucial for filtering out less significant information in the final representation of contexts and justifying the outputs of the model. (2) we introduce mcms as a feature to improve explainability and performance. this also helps a better understanding of the cognition of depressive individuals. (3) we demonstrate that han achieves outstanding performance and produces accurate and explainable results with a smaller number of training parameters than classical encoders via extensive experiments. ## related work traditional studies on depression focus on social, psychological and biological factors, which are not often readily available. this paper mainly focuses on social media texts and machine-learning-based depression detection. several studies in psychology have reported that conceptual metaphors are used to express and understand the experience of depression, but they are often used unconsciously and pass unnoticed. research into metaphors can help better understand individuals with depression. depression detection on social media. @xcite proposed a model combining cnns @xcite and bigrus @xcite for learning users' behavior and textual contents. for user behavior modeling, manually curated features, which are associated with emotions, domains, topics and social media metadata, were employed. some research exploited sentiment analysis techniques for depression detection. @xcite proposed a hierarchical architecture leveraging gated units and cnns to learn textual contents of social media posts and users' emotional states expressed in posts. @xcite proposed an emotion-aware svm-based model which learns emotional dynamics expressed in social media posts. @xcite proposed 90 features, based on sentiment lexicons and textual contents and used them as input to depression detection classifiers. a recent trend is to exploit multimodal learning frameworks for depression detection @xcite @xcite . @xcite proposed a multimodal multi-agent reinforcement learning model incorporating bigru and vggnet @xcite to learn texts and images posted by users on twitter, respectively. @xcite proposed a multimodal bilstm-based @xcite architecture jointly learning texts, images and temporal behaviors (i.e., time intervals between posts) on instagram. lin et al. (2020) proposed a multimodal model comprising a cnn and a bert. it jointly learns representations of images and texts and fused them using a lowrank multimodal fusion method. zhang et al. (2021) proposed a model combining bilstm and cnn based on metaphor features and text. however, their metaphor features are shallow, e.g., part-of-speech (pos) tags and the number of metaphors. metaphor understanding. traditional metaphor studies on depression were mainly based on qualitative analysis and case studies @xcite . this is because of a lack of automatic tools that help psycholinguistic researchers parse and analyze metaphorical expressions from large corpora. recently, automatic metaphor processing has achieved significant developments. metaphors can be identified with sequence-tagging-based models @xcite @xcite . then, the identified metaphors can be interpreted by linguistic meanings @xcite @xcite or concept mappings @xcite @xcite . linguistic metaphor interpretation focuses on paraphrasing metaphors into their lit-eral counterparts. for example, @xcite proposed a system for metaphor identification and interpretation, called metapro. it can be used as a text pre-processing technique to achieve metaphor paraphrases from end to end. thus, nlp techniques for downstream tasks, such as sentiment analysis @xcite or machine translation @xcite , can achieve better performance on the effectiveness of metaphor paraphrasing. however, @xcite argued that metaphor is not only a linguistic phenomenon, but also a reflection of humans' cognition. given "this is the core foot_1 of the matter", core implies "importance" (target) is "interiority" (source) @xcite . thus, one can analyze the inner world of depressed people from their metaphoric expressions and the associated concept mappings, e.g., ## ethical considerations this research work was conducted based on a public dataset published by @xcite . we solely used textual content for concept mapping acquisition, training, and evaluating the model. we did not leverage any information related to user profiles. we oppose the use of our model in any breach of data security, privacy protection, and ethics. ## conclusion while most deep learning architectures for depression detection left the impact of different input features on model performance inexplicable, our work attempted to interpret what was going on in the model and justify model predictions. we proposed an attention-based encoder to better understand decision-making process for depression detection. we introduced novel metaphor concept mapping features into our model to investigate how depressed people describe their emotions and experiences. our extensive experiments and comparative evaluations showed that our model could achieve sota performance. an ablation study proved the advantage of utilizing metaphors in depression detection. we argue that a better understanding of metaphors associated with depression can enhance interpretability and help health professionals provide tailored, timely therapy to patients. in future research, we plan to conduct a large-scale study to categorize different characteristics of depression using users' metaphorical and cognitive expressions.