English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automatic depression detection on Twitter can help individuals privately andconveniently understand their mental health status in the early stages beforeseeing mental health professionals. Most existing black-box-like deep learningmethods for depression detection largely focused on improving classificationperformance. However, explaining model decisions is imperative in healthresearch because decision-making can often be high-stakes and life-and-death.Reliable automatic diagnosis of mental health problems including depressionshould be supported by credible explanations justifying models' predictions. Inthis work, we propose a novel explainable model for depression detection onTwitter. It comprises a novel encoder combining hierarchical attentionmechanisms and feed-forward neural networks. To support psycholinguisticstudies, our model leverages metaphorical concept mappings as input. Thus, itnot only detects depressed individuals, but also identifies features of suchusers' tweets and associated metaphor concept mappings.

Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings