4 research outputs found
Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media
The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction
Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia
The recent COVID-19 pandemic has caused unprecedented impact across the
globe. We have also witnessed millions of people with increased mental health
issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety,
which have become one of the major public health concerns during this severe
health crisis. For instance, depression is one of the most common mental health
issues according to the findings made by the World Health Organisation (WHO).
Depression can cause serious emotional, behavioural and physical health
problems with significant consequences, both personal and social costs
included. This paper studies community depression dynamics due to COVID-19
pandemic through user-generated content on Twitter. A new approach based on
multi-modal features from tweets and Term Frequency-Inverse Document Frequency
(TF-IDF) is proposed to build depression classification models. Multi-modal
features capture depression cues from emotion, topic and domain-specific
perspectives. We study the problem using recently scraped tweets from Twitter
users emanating from the state of New South Wales in Australia. Our novel
classification model is capable of extracting depression polarities which may
be affected by COVID-19 and related events during the COVID-19 period. The
results found that people became more depressed after the outbreak of COVID-19.
The measures implemented by the government such as the state lockdown also
increased depression levels. Further analysis in the Local Government Area
(LGA) level found that the community depression level was different across
different LGAs. Such granular level analysis of depression dynamics not only
can help authorities such as governmental departments to take corresponding
actions more objectively in specific regions if necessary but also allows users
to perceive the dynamics of depression over the time
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media
AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.</jats:p