Multimodal Topic-Enriched Auxiliary Learning for Depression Detection

Proceedings of the 28th International Conference on Computational Linguistics, pages 1078–1089 Barcelona, Spain (Online), December 8-13, 2020 1078 Multimodal Topic-Enriched Auxiliary Learning for Depression Detection Minghui An, Jingjing Wang∗, Shoushan Li, Guodong Zhou School of Computer Science and Technology, Soochow University, China mhan@stu.suda.edu.cn, {djingwang, lishoushan, gdzhou}@suda.edu.cn Abstract From the perspective of health psychology, human beings with long-term and sustained negativity are highly probable to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r.t. the two modalities are cast as two auxiliary tasks for improving the performance of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information. 1 Introduction Depression detection is a task of determining a human being is depressed or non-depressed by automat- ically analyzing user-generated contents (UGC). Due to its crucial role in assessing mental health, it has recently received considerable attention from several research communities, such as NLP (Shen et al., 2017) and CV (Valstar et al., 2016). These studies mainly utilize UGC (e.g., texts and images) on social media to perform depression detection and achieve promising results since UGC instantly reflects not only the daily lives but also the mental states of users. Despite the progress of prior studies, they always focus on leveraging RNN variants (e.g., GRU) to model texts or images along the timeline (see Figure 1) posted by users, suffering from the problem of ignoring the global topic information inside these texts and images, though obviously this global topic information can mitigate the notorious difficulty of RNN variants w.r.t. modeling long-range dependencies (Dieng et al., 2017). More importantly, from the point of health psychology, humans plagued by the negative emotion for a prolonged period of time (generally longer than two weeks), leading to their inability of carrying out daily activities, are highly probable to have the depression symptom (American Psychiatric Association and others, 2013). For instance, Figure 1 illustrates a month-long timeline of a depressed user. From this figure, we can see that this person is highly possible to be depressed since he/she discloses negative emotions lasting for almost a month. This conforms to his/her depression symptom and indicates the importance of considering the global semantics for depression detection. Inspired by the …

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